# Heista (remote · www.heista.co) Decode video ads, load brand intelligence, generate ad scripts. - Trust score: 76/100 (medium) - Change this week: +8 - Registry status: active - Liveness: live - Owner verified: no - Last scored: 2026-08-03 ## Components - remote · `www.heista.co`: 76/100 (this document), [markdown](https://verifymcp.io/servers/co-heista-api/api-mcp-mcp.md), [page](https://verifymcp.io/servers/co-heista-api/api-mcp-mcp) ## Channel facts - Endpoint: `https://www.heista.co/api/mcp/mcp` - Transports: `streamable-http` - Auth: `none` - Version: `1.0.0` ## Trust breakdown How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog) Scored 2026-08-03. - **Endpoint Security**: 94/100 - The endpoint's TLS certificate is valid, in date, and uses a strong key. - Authorisation is enforced on tool calls, advertised via RFC 9728 protected-resource metadata. Discovery is public, which costs nothing: no tool can be invoked without a token. - HTTPS is enforced; there's no plaintext access path. - The HSTS (Strict-Transport-Security) header is present. - DNSSEC check failed: this domain isn't protected by DNSSEC. - The authorisation server offers only Dynamic Client Registration (RFC 7591), which MCP 2026-07-28 deprecated in favour of Client ID Metadata Documents. - **Transport & Reachability**: 100/100 - Verified streamable-http transport via a live MCP handshake. - **Schema Quality & AI Usability**: 48/100 - 97% of prompts and resources have a non-trivial description (not blank, and not just the item's name). - AI-judged instruction clarity (poor). - Context-footprint check failed: tool/resource definitions use about 29417 tokens (~260/item across 113 items; 112 tools + 1 resources), over budget; trim descriptions and params. - Usage-examples check failed: none of the tools include examples. - **Stability & Change Management**: 27/100 - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses. - **Tool Coverage**: 100/100 - 100% of tools have a non-trivial description (not blank, and not just the tool's name). - 99% of tool parameters carry a description. - **Capabilities**: 100/100 - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28. - Supports UI / widget rendering. ## Install ### Claude ```bash claude mcp add --transport http co-heista-api https://www.heista.co/api/mcp/mcp ``` ### Codex ```toml [mcp_servers.co-heista-api] url = "https://www.heista.co/api/mcp/mcp" ``` ### opencode ```json { "$schema": "https://opencode.ai/config.json", "mcp": { "co-heista-api": { "type": "remote", "url": "https://www.heista.co/api/mcp/mcp", "enabled": true } } } ``` ### OpenClaw ```bash openclaw mcp add co-heista-api --url https://www.heista.co/api/mcp/mcp --transport streamable-http ``` ### Hermes ```yaml mcp_servers: co-heista-api: url: "https://www.heista.co/api/mcp/mcp" ``` ### Other ```json { "mcpServers": { "co-heista-api": { "type": "http", "url": "https://www.heista.co/api/mcp/mcp" } } } ``` The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs. ## Changelog Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims. ### 2026-08-02 (score 76, +1) No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes. ### 2026-07-31 (score 75, +7) - [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server ### 2026-07-30 (score 68, −1) - [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server ### 2026-07-29 (score 69, +1) - [security] New tool “creative_publish_article”, which the server declares destructive - [functional] New tool “creative_list_articles” - [functional] New tool “creative_save_draft” - [functional] New tool “fleet_product_signups_recent” - [functional] New tool “fleet_product_user_summary” - [functional] New tool “fleet_product_funnel_summary” - [functional] New tool “creative_get_authoring_contract” - [functional] New tool “creative_get_draft” ### 2026-07-27 (score 68, +1) - [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server ### 2026-07-26 (score 67) First indexed and scored. ## MCP tools (112) ### `decode_ad` (~311 tokens) Decode Video Ad Decode a specific video ad URL into its full structural formula — beat-by-beat breakdown, hook classification, behavioral psychology stack, creative format, runtime performance signals (active days on Meta Ad Library when available), and per-cut visual data. Takes one video URL plus an optional idempotency_key. Returns a job_id immediately; poll with get_decode every 15s until status is "completed" (typically 45-60s end-to-end). Use this when the user pastes an ad URL, names a specific competitor ad, asks "decode this" or "break down this ad" or "what makes this ad work", or wants sentence-level fidelity to one specific winner before writing a script with generate_adscript. Supports Facebook Ad Library, TikTok, Instagram Reels, YouTube Shorts, and direct .mp4 URLs. Costs 15 credits for videos ≤60s, 20 credits for 61-120s. Do NOT use to browse the corpus or find ads by category — use decoder_intelligence or adformula_intelligence (both free) for discovery. Do NOT use for image ads or static creative. Input parameters: - `idempotency_key` (string): Optional unique key to make this call safely retryable. If the same key + org repeats, the original result is returned without re-charging. - `url` (string, required): Video URL to decode. Supports: Facebook Ad Library, TikTok, Instagram Reels, YouTube Shorts, or direct .mp4 URL. ### `get_decode` (~232 tokens) Get Decode Result Retrieve the full decode bundle for a previously-submitted ad, or poll the status of a running decode job. Takes a single job_id (UUID returned by decode_ad). Returns either status="processing" (call again in 15s) or the completed payload — exact transcripts per beat, director's read, per-cut visual data (shot_breakdown), visual psychology, behaviour biases, beat structure, hook classification, and runtime fields (active days on Meta Ad Library when the source supports it). Use this immediately after decode_ad and every 15 seconds until the job completes. Also use this to re-fetch a decode any time you need the full bundle for script writing (Path B) or as the source_id for generate_adscript (source_type="decode"). Free — billing happens at decode_ad submit time, not on retrieval. Do NOT use to discover or list decodes — use decoder_intelligence for browsing. Do NOT use to start a new decode — call decode_ad first. Input parameters: - `job_id` (string, required): Job ID returned by decode_ad. Call this tool to poll status or retrieve completed results. ### `create_powersource_url` (~491 tokens) Create PowerSource from URL Build a complete creative intelligence profile of a brand from a single website URL. Takes a website URL (homepage, PDP, landing page) plus optional idempotency_key, force_refresh, and webhook_url. Returns a job_id immediately; poll with get_powersource every 3-5s (typically 60-90s total). The final payload contains 14 structured sections: identity, offer, selling_points, brand_story, brand_style, brand_assets, brand_voice, buyer_profile, 12 buyer tensions, marketing angles, emotional_arcs, ctas, proof_assets, and strategic narrative. Use this when the user says "analyse my brand", "load my brand", "build a strategy from my site", "what should my ads say", "decode this website", or pastes a homepage / competitor URL and wants a brand profile (not an ad decode). Also use this as the brand layer before calling generate_adscript — pass the returned powersource_id. Costs 100 credits. Re-scanning the same URL within your org returns the cached result free. Do NOT use for internal docs / PDFs / brand guidelines — use create_powersource_docs. For URL + docs combined (highest fidelity), use create_powersource_full. Do NOT use to decode a video ad — use decode_ad. Input parameters: - `brand_id` (string): Optional Brand to attach this scan to. Get from list_brands. When omitted, the pipeline auto-resolves a brand by the scanned domain (creating one if needed) — the addendum D6 default. Pass this when… - `force_refresh` (boolean): Force re-extraction of brand data even if cached. Use when a brand has rebranded or updated their website. - `idempotency_key` (string): Optional unique key to make this call safely retryable. If the same key + org repeats, the original result is returned without re-charging. - `url` (string, required): Website URL to analyze. Supports any public website (e.g., gymshark.com, notion.so). Bare domains auto-resolve to https. - `webhook_url` (string): HTTPS URL to receive a POST notification when the scan completes or fails. Eliminates need for polling. ### `get_powersource` (~306 tokens) Get PowerSource Result Retrieve the full creative intelligence profile for a previously-submitted PowerSource scan, or poll the status of a running scan. Takes a job_id (UUID returned by any create_powersource_* tool) plus an optional include_raw flag (admin-only). Returns either status="processing" with partial progress or the completed bundle: brand identity, offer, 12 selling points, brand voice rules, buyer profile, 12 buyer tensions, angles, emotional arcs, ctas, proof, narrative. Use this immediately after any create_powersource_* call and every 3-5 seconds until status is "completed". During synthesis, partial intelligence appears progressively (buyer archetype first, then tensions, then angles) — inspect each poll response, useful signal arrives early. Also use this to re-fetch a finished PowerSource any time you need the brand layer for downstream work. Free — billing happens at submit time. Do NOT use to start a new scan — call create_powersource_url, _docs, or _full first. Do NOT use to retrieve a video decode — use get_decode. Input parameters: - `include_raw` (boolean): Internal-only. When true and the caller holds mcp:internal_admin, returns the un-merged brief bundle alongside the merged response. Silently ignored for non-admin callers — no error is raised. - `job_id` (string, required): Job ID returned by any create_powersource_* call. Use this to poll status or retrieve completed results. ### `create_powersource_docs` (~622 tokens) Create PowerSource from Documents Build a complete creative intelligence profile from internal brand documents — creative briefs, brand guidelines, product specs, customer research, competitive analysis. Takes any mix of file_ids (from a previous upload), document_urls (public PDF/DOCX/TXT/MD links, up to 10), or documents_inline (base64-encoded files with filename), plus an optional context_url for layering live brand context (colors, fonts, current messaging) and optional idempotency_key. Returns a job_id; poll with get_powersource. Output shape is identical to create_powersource_url: identity, offer, selling points, voice, buyer profile, tensions, angles, emotional arcs, ctas, narrative. Use this when the user says "I have a brief", "here's my brand guidelines", "use this document", drops a PDF / DOCX / strategy deck, or when the truth lives in internal materials rather than the public website. The pipeline reads text only — convert PDFs to markdown before submitting via documents_inline when possible. Costs 100 credits. Do NOT use for URL-only scans — use create_powersource_url. For URL + docs combined (highest fidelity, triangulates public messaging against internal strategy), use create_powersource_full. Input parameters: - `brand_id` (string): Optional Brand to attach this scan to. Get from list_brands. When omitted, the pipeline auto-resolves a brand by the context_url domain (if provided) or creates a standalone scan with no brand link. - `context_url` (string): Optional website URL to layer live brand context on top of the documents (colors, fonts, current messaging). - `document_urls` (array): Array of public URLs pointing to documents (PDF, DOCX, TXT, MD). Up to 10 URLs. - `documents_inline` (array): Inline documents as base64. Use when the user has uploaded a file into chat and no public URL exists. IMPORTANT: The synthesis pipeline reads TEXT ONLY — it ignores images, diagrams, and visual layou… - `file_ids` (array): Array of file IDs from a previous upload. Up to 10 files. - `idempotency_key` (string): Optional unique key to make this call safely retryable. If the same key + org repeats, the original result is returned without re-charging. ### `create_powersource_full` (~438 tokens) Create PowerSource Full (URL + Documents) Build the highest-fidelity creative intelligence profile by combining a brand's public website URL with their internal documents. Takes a required website URL plus at least one document — file_ids from previous upload, public document_urls (PDF/DOCX/TXT/MD, up to 10), or documents_inline (base64-encoded). Optional idempotency_key for safe retry. Returns a job_id; poll with get_powersource. Same response shape as create_powersource_url, but the synthesis cross-checks how the brand presents publicly against what the team actually believes internally, producing stronger conviction on voice, positioning, proof, and tension architecture than either input alone. Use this when the user has both a public site AND a brief / brand guidelines / strategy deck and wants the deepest possible profile — the kind of intelligence a senior strategist produces over a week. Default recommendation when both inputs are available. Costs 200 credits. Do NOT use for URL-only scans — use create_powersource_url (100 credits). Do NOT use for docs-only scans — use create_powersource_docs (100 credits). Input parameters: - `brand_id` (string): Optional Brand to attach this scan to. Get from list_brands. When omitted, the pipeline auto-resolves a brand by the URL domain (creating one if needed). - `document_urls` (array): Array of public URLs pointing to documents (PDF, DOCX, TXT, MD). Up to 10 URLs. - `documents_inline` (array): Inline documents as base64. The pipeline reads TEXT ONLY — for any PDF or DOCX, extract the text content first using your file-reading tools, save as .md or .txt, then base64-encode and submit here.… - `file_ids` (array): Array of file IDs from a previous upload. Up to 10 files. - `idempotency_key` (string): Optional unique key to make this call safely retryable. - `url` (string, required): Website URL to analyze. Supports any public website. REQUIRED. ### `check_balance` (~204 tokens) Check Balance Check the calling user's Heista API credit balance, month-to-date usage broken down by operation, lifetime spend, and the current pricing for every paid tool. Takes no inputs. Returns balance in cents, lifetime spend in cents, month-to-date call counts per tool (decode_ad, create_powersource_*, generate_adscript), per-tool unit pricing, and a top-up link the user can follow to add credits. Free, read-only, idempotent. Use this whenever the user asks about credits, balance, usage, how much they've spent, top-ups, pricing, "what does this cost", or "how many credits do I have". This is also the ONLY surface where dollar amounts are legitimate to report in conversation — everywhere else, cost should be referenced in credits, not currency. Do NOT use to add credits or change billing — only to read state. Do NOT call this on every turn — invoke once when the user explicitly asks about account state. ### `get_hook_intelligence` (~446 tokens) Get Hook Intelligence Browse proven hook patterns from Heista's corpus of decoded winning Meta/TikTok ads. Takes optional filters: vertical (e.g. BEAUTY_SKINCARE, SUPPLEMENTS, APPAREL), hook_type (e.g. CURIOSITY_SPIKE, CONTRADICTION, CALLOUT), and marketing_angle. Returns hook examples (the real opener lines from successful ads), pattern templates, the psychological mechanism behind why each one stops the scroll within the first 1.5 seconds, and runtime performance data (active days on Meta when available). Free, read-only, idempotent — no credits consumed. Use this when the user asks "what hooks stop the scroll", "give me hook ideas", "how should I open this ad", "show me hooks for [vertical]", or needs scroll-stopping openers grounded in proven patterns rather than guessed copy. Useful before writing a script — pair with adformula_intelligence or decoder_intelligence for the full beat structure. Do NOT use to decode a specific ad URL — use decode_ad. Do NOT use to generate finished scripts — use generate_adscript. Hooks here are pattern intelligence, not finished copy. Input parameters: - `hook_type` (string): Specific hook type to retrieve patterns for. Examples: CURIOSITY_SPIKE, OPEN_LOOP_STATEMENT, HIDDEN_TRUTH_REVEAL, IDENTITY_HOOK, CONTRADICTION_HOOK, PROVOCATION, STORY_START, DIRECT_QUESTION_HOOK, CH… - `marketing_angle` (string): Marketing angle to filter by. Examples: PROBLEM_SOLUTION, SOCIAL_PROOF_RESULTS, HOW_TO_TUTORIAL, OFFER_URGENCY, ASPIRATIONAL_IDENTITY, VALUE_STACK. Omit for all angles. - `vertical` (string): Industry vertical to filter corpus patterns. Examples: BEAUTY_SKINCARE, HEALTH_SUPPLEMENTS, FITNESS, FOOD_BEVERAGE, FASHION_APPAREL, SAAS_SOFTWARE, FINANCE_FINTECH, INFO_PRODUCTS, TECH_GADGETS. Omit… ### `adformula_intelligence` (~599 tokens) Ad Formula Intelligence Browse proven ad formula blueprints — structural patterns clustered from 3-10+ winning ads that independently converged on the same beat architecture while Meta kept rewarding them with sustained spend. Takes optional filters: vertical, creative_format (e.g. TALKING_HEAD, UGC, FOUNDER_STORY), marketing_angle, algo_intent, hook_type, and limit (1-10, default 5). Each formula returns: source ad count, average active days (runtime proof), confidence score, 6-layer beat blueprint, per-beat visual direction, marketing angle, psychology mission. Free, read-only, idempotent. Use this when the user asks "what's working in [category]", "show me formulas for talking-head ads", "what scripts work in my vertical", or wants category-level pattern discovery before committing to a single ad. Pass the returned formula id to generate_adscript with source_type="formula" for synthesis. When choosing among results: prioritise (1) avg_active_days as primary proof, (2) marketing_angle alignment with the brand's buyer tension, (3) source_ad_count for cluster robustness, (4) confidence_score as tiebreaker. Do NOT use when the user names a specific ad — decode that ad with decode_ad. Do NOT use for sentence-level transcript fidelity — formulas abstract the structure, not exact copy. Input parameters: - `algo_intent` (string): Structural engine to filter by. Examples: PROBLEM_AGITATE_SOLVE, MECHANISM_REVEAL, TRANSFORMATION_ARC, SOCIAL_PROOF_STACK, COMPARISON_CONTRAST, URGENCY_SCARCITY. Omit for all intents. - `creative_format` (string): Creative format to filter by. Examples: TALKING_HEAD_BROLL, VOICEOVER_BROLL, UGC_TESTIMONIAL, PRODUCT_DEMO, SLIDESHOW_OVERLAY, INFLUENCER. Omit for all formats. - `hook_type` (string): Filter by opening hook subtype. Examples: CURIOSITY_SPIKE, IDENTITY_HOOK, CONTRADICTION_HOOK, DIRECT_QUESTION_HOOK, PAST_SELF_OPEN, DATA_POINT_START, PROVOCATION. Omit for all hook types. - `limit` (integer): Max formulas to return (1-10, default 5). - `marketing_angle` (string): Marketing angle to filter by. Examples: PROBLEM_SOLUTION, SOCIAL_PROOF_RESULTS, HOW_TO_TUTORIAL, INGREDIENT_SCIENCE, ASPIRATIONAL_IDENTITY, VALUE_STACK. Omit for all angles. - `vertical` (string): Industry vertical to filter formulas. Examples: BEAUTY_SKINCARE, HEALTH_SUPPLEMENTS, FITNESS, FOOD_BEVERAGE, FASHION_APPAREL, SAAS_SOFTWARE, FINANCE_FINTECH, INFO_PRODUCTS, TECH_GADGETS. Omit for all… ### `decoder_intelligence` (~605 tokens) Decoder Intelligence Browse individual decoded ads from Heista's corpus of real winning Meta/TikTok creative. Takes optional filters: vertical, creative_format, marketing_angle, hook_type, algo_intent, brand (partial name match), and limit (1-10, default 5). Each result returns beat timeline, classification, psychology, runtime performance signals (active days on Meta when available), and a decode id you can pass into generate_adscript with source_type="decode" to write a fresh script on that exact structure. Free, read-only, idempotent — no credits consumed. Use this when the user wants a specific ad as a script template (not an averaged formula), asks "show me winning ads in [vertical]", "what are [brand]'s top ads", or wants to see examples before committing to a generation. Source discovery surface — the response is the spine; for the full bundle with transcripts and director's read, call get_decode by id afterwards. Do NOT use to decode a NEW ad from a URL — use decode_ad (paid). Do NOT use for category-level patterns abstracted across multiple ads — use adformula_intelligence. Do NOT use to write the script itself — use generate_adscript or write directly from the bundle. Input parameters: - `algo_intent` (string): Structural engine to filter by. Examples: PROBLEM_AGITATE_SOLVE, MECHANISM_REVEAL, TRANSFORMATION_ARC, SOCIAL_PROOF_STACK, COMPARISON_CONTRAST, URGENCY_SCARCITY. Omit for all intents. - `brand` (string): Filter by brand name (case-insensitive partial match). Examples: "Gymshark", "AG1", "Huel". Omit for all brands. - `creative_format` (string): Creative format to filter by. Examples: TALKING_HEAD_BROLL, VOICEOVER_BROLL, UGC_TESTIMONIAL, PRODUCT_DEMO, SLIDESHOW_OVERLAY, INFLUENCER. Omit for all formats. - `hook_type` (string): Filter by opening hook type. Examples: CURIOSITY_SPIKE, IDENTITY_HOOK, CONTRADICTION_HOOK, PROVOCATION, STORY_START, DIRECT_QUESTION_HOOK. Omit for all hook types. - `limit` (integer): Max decoded ads to return (1-10, default 5). - `marketing_angle` (string): Marketing angle to filter by. Examples: PROBLEM_SOLUTION, SOCIAL_PROOF_RESULTS, HOW_TO_TUTORIAL, INGREDIENT_SCIENCE, ASPIRATIONAL_IDENTITY, VALUE_STACK. Omit for all angles. - `vertical` (string): Industry vertical to filter decoded ads. Examples: BEAUTY_SKINCARE, HEALTH_SUPPLEMENTS, FITNESS, FOOD_BEVERAGE, FASHION_APPAREL, SAAS_SOFTWARE, FINANCE_FINTECH, INFO_PRODUCTS, TECH_GADGETS. Omit for… ### `generate_adscript` (~825 tokens) Generate Ad Script Generate direct-response video ad scripts by fusing a proven structural source (decoded ad or formula) with a brand's PowerSource. Output is feed-native ad copy for paid social (Meta, TikTok, Reels) in the brand's voice — hook, beat-by-beat body, CTA close, plus visual direction per beat. Takes source_id (from adformula_intelligence, decoder_intelligence, or decode_ad), source_type ("formula" or "decode"), powersource_id (from any create_powersource_*), and tunable params: count (1-5 variants, tensions and selling points auto-rotated across variants), script_mode ("blueprint" preserves source structure exactly, "remix" preserves psychology but writes original copy), duration (target seconds), audience, tension override, selling_points override, voice_mode ("creator" for UGC default, "brand" for owned channels), and idempotency_key. Use this when the user says "write me a script", "I need a TikTok script", "write an ad based on this", or wants shell-faithful replication of a proven winner in their own brand voice. REQUIRES both a structural source AND a powersource — guide the user through creating either if missing. Metered pricing — typically 2-5 credits per script (~2 credits for 15s, ~5 credits for 60s). Pre-flight reserves a 17-credit ceiling and refunds the difference after measurement. Do NOT use to discover sources — use decoder_intelligence or adformula_intelligence first. Do NOT use to extract brand intel — use create_powersource_url first. Input parameters: - `audience` (string): Audience segment from the PowerSource. "buyer_profile" (default) uses the composite buyer. "audience_0", "audience_1", etc. target specific segments. - `count` (integer): Number of scripts to generate (1-5, default 1). Each script uses a different tension and selling point combination for variety. - `duration` (integer): Target duration in seconds (remix mode only, 10-120). Blueprint mode locks to the source duration. - `idempotency_key` (string): Optional unique key to make this call safely retryable. If the same key + org repeats, the original result is returned without re-charging. - `powersource_id` (string, required): Identifier for the brand PowerSource that supplies voice, selling points, tensions, and audience. Accepts either a job_id from create_powersource_* or a brief_id from get_powersource — both work. - `script_mode` (string): Script mode. "blueprint" (default) follows the source formula exactly — same beat structure, same timing. "remix" uses the psychological architecture but writes original copy. - `selling_points` (array): Lock to specific selling points from the PowerSource (max 5). Omit to let the system select the best match for each beat. - `source_id` (string, required): The ID of the structural source to write from. For source_type="decode": either a job_id from your own decode_ad call OR an id from decoder_intelligence (corpus ad). For source_type="formula": a form… - `source_type` (string, required): Type of structural source. "decode" = a single decoded ad (your own or from the corpus). "formula" = a clustered blueprint built from multiple winning ads. - `tension` (string): Lock to a specific behavioral tension from the PowerSource (e.g., "Frustration → Relief"). Omit to let the system select the best match. - `voice_mode` (string): Voice register for the script. "creator" (default) = authentic creator voice for UGC, PowerSource locks facts/tensions/selling points but NOT voice register. "brand" = full PowerSource brand voice fo… ### `call_creative_worlds` (~986 tokens) Call Creative Worlds Heista's creative direction engine — same engine the Creative Director specialist runs internally, exposed over MCP. ONE-SHOT: give a brief, get N finished creative outputs. For back-and-forth refinement, or output shapes the `medium` enum below does not cover, use chat_with_creative_worlds instead. OUTPUT SHAPE switches on the `medium` arg: • omitted → N territory cards (default exploration). Each card sits on different psychology / craft / feel / world axis coordinates so the set spans the creative space rather than orbiting one insight. Card has: name, campaign line, 5-8 sentence pitch, one-sentence strategic bet, resolved axis state names, creative-director rationale. • `tvc` → N TVC scripts (15-90s — hook, arc, resolve, sound design, end line). • `billboard` / `ooh` / `print` → N out-of-home concepts (visual concept + line + placement rationale). • `social` → N social-video concepts (hook + format type + middle beat + payoff, optimised for Reels / TikTok / Shorts). • `activation` / `experiential` → N activation concepts (space design + user journey + peak moment + takeaway artifact). • `audio` → N sonic / radio concepts (sonic scene + voice + audio arc). • `campaign` → N full campaign platforms (insight → big idea → strategy → visual world → production roadmap). The engine can also produce manifesto / copy, naming, packaging, PR stunts, content series, brand positioning, partnerships — these output shapes are NOT in the medium enum, so use chat_with_creative_worlds when the user wants one of those. USE WHEN: user says "give me ideas / options / directions / territories", "what angles work for...", "show me three / five ways to...", "write a TVC for...", "draft billboard concepts for...", "I need fresh thinking on...". DO NOT USE to refine one existing direction (use chat tool), to critique work, for OKRs / internal docs / strategy decks, or anything outside advertising creative direction. INPUTS: brief (the creative problem, free text), count (2-6 concept… Input parameters: - `brand_id` (string): Optional Heista brand id (a.k.a. brief id) to ground the territories in. Get from list_brands or any create_powersource_* call. When provided, the engine pulls the brand intelligence (buyer tensions,… - `brief` (string, required): The creative brief — what you want territory directions for. One sentence or short paragraph. Example: "Hero campaign for a sparkling water brand launching in Australia, positioned against soft drink… - `count` (integer, required): How many distinct creative territories to generate. Each will sit on different axis coordinates from the others — different psychology, different feel, different world. 2-6. - `idempotency_key` (string): Optional unique key to make this call safely retryable. If the same key + org repeats within 5 minutes, the original result is returned without re-charging. - `lens_hint` (object): Optional creative lens to constrain the direction. Applied throughout the ideation as a creative constraint. Use when an agent has already picked a playbook or signature move and wants territories un… - `medium` (string): Optional medium — switches the output shape. Omit → territory cards (default exploration). `tvc` → TVC scripts (hook + arc + resolve + sound + end line). `billboard` / `ooh` / `print` → out-of-home c… ### `call_creative_agent_preset` (~568 tokens) Call Creative Agent Invoke a Creative Agent (character) preset. Every preset is a purpose-built character the workspace has authored or the Heista catalog has published — copy voice, art direction, strategy, creative direction, etc. ONE-SHOT: give the character a message, get its response back as text. Discover callable presets via list_creative_agent_presets. Workspace-authored presets are only callable inside their owning org; official templates (visibility=public_template) are callable from any authenticated org. INPUTS: agent_id (UUID from list_creative_agent_presets), message (the turn text), optional brand_id (server-loads multi-strategy brand summary for character context), optional working_context (light labels the character reads as IN SCOPE — pinned brand + strategies + documents + playbook), optional thread_id (continuity id), optional idempotency_key (5-minute retry safety). Returns the character's response as plain text plus a structured envelope with usage, model, provider, thread_id. Metered — cost depends on character model + context size, typically 2-10 credits per turn. Charged after success on real token usage. Input parameters: - `agent_id` (string, required): The Creative Agent preset id to call. Discover ids via list_creative_agent_presets. Workspace-authored agents are only callable from within the owning org; official templates (visibility=public_templ… - `brand_id` (string): Optional Heista brand id (a.k.a. brief id). When provided, the runtime server-loads the multi-strategy brand summary via `loadMultiBrandContext` and injects it into the agent's system prompt as BRAND… - `idempotency_key` (string): Optional unique key to make this call safely retryable. If the same key + org repeats within 5 minutes, the cached response returns without re-charging. - `message` (string, required): The user turn — the message the caller wants the character to respond to. One-shot; not persisted as a conversation history unless the caller supplies thread_id continuity. - `thread_id` (string): Optional continuity id round-tripped on the response. Callers manage their own thread state — the runtime does NOT persist history for library calls (chat surface uses its own session table). - `working_context` (object): Optional Working Context labels — brand pin, pinned strategies, pinned documents, loaded playbook. Rendered into the agent's system prompt as an IN SCOPE bullet block so the agent knows what the work… ### `chat_with_creative_worlds` (~594 tokens) Chat with Creative Worlds Multi-turn conversation with Heista's creative direction engine — a real chat where the agent decides each turn what to produce based on what you ask for. Use whenever the work needs more than one round, OR when you want an output shape not covered by call_creative_worlds' `medium` enum. WHAT YOU CAN ASK FOR (any of these, turn 1 or any turn after): • Territories — "give me five directions for X", "what angles work here" • A TVC script — "write a 30-second TVC for Cowboys" • Billboard concepts — "three billboards under a quiet-authority lens" • A campaign platform — "build #2 into a full campaign with the big idea" • A manifesto or copy — "draft the manifesto in the brand voice" • Naming — "name this product, five options with rationale" • A PR stunt — "what's the newsworthy version of this" • A content series — "20 episode ideas for a brand podcast" • Packaging, sonic branding, partnerships, social systems • Refinement — "make #2 darker", "extend that into a tagline", "summarise" • Pivots — "forget the soft-drink angle, try the late-night insomnia one" SESSION: omit session_id on turn 1; the response returns a fresh session_id you pass on every subsequent turn — that is how the conversation persists. brand_id is only honoured on turn 1 of a new session (continuing sessions keep their original brand context). USE WHEN: user wants back-and-forth, OR wants an output shape outside the medium enum (manifesto, naming, press release, content series, packaging, etc.). Prefer call_creative_worlds when the user wants "three options, done" with no follow-up. WON'T DO: write OKRs / internal docs / strategy decks; behave as a general assistant. It is a creative director with creative-director taste — anti-cliché, specificity test, will push back on vague briefs. Metered — typically 2-10 credits per turn depending on tool use and context size. Charged after each turn on actual token usage. Input parameters: - `brand_id` (string): Optional Heista brand_id to ground the conversation in. Only honoured on the first turn of a new session (continuing sessions keep their original brand context). - `message` (string, required): The principal's message to the Creative Worlds specialist. First turn: a brief or open question. Subsequent turns: refinement ("make #2 darker"), filtering ("summarise that"), extension ("build a tag… - `session_id` (string): Pass the session_id returned from a previous chat_with_creative_worlds call to continue that conversation. Omit to start a new session — the response will include a fresh session_id you should pass o… ### `list_brands` (~205 tokens) List Brands List every brand in this workspace. Use this BEFORE creating a PowerSource to avoid creating duplicate brand records (pass the matching brand_id to create_powersource_*), and to discover brands the user can pivot a Heist to. Each row carries the brand_id (persistent identity), name, domain, asset_count, strategy_count, and brand status. Use this when the user asks "what brands do I have", "show me my brands", or before any image-led work where you need to know which brand owns assets. Free, read-only. Distinguish Brand (persistent, brand_id) from PowerSource (a scan, powersource_id). A brand has many PowerSources; pick the brand first, then narrow to a strategy with list_strategies. Input parameters: - `include_all` (boolean): When true, returns transient (status="creating") and signal-less draft brands too. Default false matches the picker dropdown — only confirmed/draft brands with real data. ### `get_brand` (~91 tokens) Get Brand Get a brand's full canonical record — name, domain, voice (tone_of_voice), story, visual identity (logo, primary color, visual assets), and counts. Use to inspect what a brand carries before deciding which Heist context to run, or to read the brand voice directly when writing copy. Free, read-only. Input parameters: - `brand_id` (string, required): Brand to inspect. Get from list_brands. ### `list_strategies` (~172 tokens) List Strategies List all PowerSource strategies (scans) for a brand. A brand has many strategies — one per scanned URL. Product-page strategies carry product_name and is_product_page=true; use these to label them in conversation or to pick the right one for a product-focused generation. Returns powersource_id (use as the brief/PowerSource id everywhere else), product_name, scanned_at, source_url, is_pinned. Free, read-only. Paginated via cursor. Input parameters: - `brand_id` (string, required): Brand to list strategies for. Get from list_brands. - `cursor` (string): Pagination cursor returned as next_cursor on the previous page. - `include_archived` (boolean): Include archived strategies. Default false. - `limit` (integer): Page size. Default 20, max 100. ### `list_projects` (~118 tokens) List Projects List all projects (campaign folders) for a brand. A project groups strategies, documents, client assets, and outputs under one campaign. Returns project_id (pass as project_id to list_strategies / list_brand_documents / list_brand_assets to scope those reads to this project), name, status, start_date, target_date, and per-type counts. Free, read-only. Input parameters: - `brand_id` (string, required): Brand to list projects for. Get from list_brands. - `include_archived` (boolean): Include archived projects. Default false. ### `list_brand_assets` (~287 tokens) List Brand Assets List images for a brand. Filter by PowerSource (this scan only, via powersource_id), by on-pack product_name (the vision tagger's read), by type (logo, product, product_cutout, hero, lifestyle, ingredient, packaging, certification, before_after, infographic, screenshot, video, general), or by is_primary_product. Use this BEFORE generating any image-based output so you pick from the brand's real assets, not generic stock. Returns asset_id, signed url, type, detected_product_name, is_primary_product, sources. Free, read-only. Paginated via cursor. Input parameters: - `brand_id` (string, required): Brand to list assets for. Get from list_brands. - `cursor` (string): Pagination cursor returned as next_cursor on the previous page. - `is_primary_product` (boolean): Filter to only the scanned product's images (or its absence with false). - `limit` (integer): Page size. Default 50, max 200. - `powersource_id` (string): Filter to assets discovered during this PowerSource scan. - `product_name` (string): Filter to assets the vision tagger read as this on-pack product name. - `type` (string): Filter by image type: logo, product, product_cutout, hero, lifestyle, ingredient, packaging, certification, before_after, infographic, screenshot, video, general. ### `add_brand_asset` (~140 tokens) Add Brand Asset Upload an image to a brand by URL. The pipeline downloads it, runs the vision tagger (classifies type, detects product name, flags is_primary_product), stores it in the brand-assets bucket, and inserts a brand_assets row. Paid (vision tag credit). If vision tagging fails, the asset is still saved with type=general and can be retried via retag_brand_asset. Input parameters: - `brand_id` (string, required): Brand to add the asset to. Get from list_brands. - `image_url` (string, required): Public HTTPS URL to fetch. The pipeline downloads, vision-tags, stores in the brand-assets bucket, and inserts a row. ### `delete_brand_asset` (~75 tokens) Delete Brand Asset Delete one brand asset by asset_id. Removes the brand_assets row and (when the asset was uploaded rather than scanned) the storage object. Destructive — confirm with the user before calling. Use list_brand_assets first to find the asset_id. Input parameters: - `asset_id` (string, required): Asset to delete. Get from list_brand_assets. ### `retag_brand_asset` (~100 tokens) Retag Brand Asset Re-run the vision tagger on one brand asset. Reads the stored object when present (uploaded assets) or the original URL (scan-sourced assets), then updates type, detected_product_name, is_primary_product, description, and the other vision fields. Useful when the original tagger run missed or misclassified an image. Paid (vision tag credit). Input parameters: - `asset_id` (string, required): Asset to re-classify. Get from list_brand_assets. ### `reclassify_brand_assets` (~158 tokens) Reclassify Brand Assets Batch re-run the vision tagger against every asset in a brand that hasn't been reviewed yet (vision_classified=false). Recovers rows the scan-time tagger dropped because of CDN blocks (Shopify hotlink, Cloudflare bot gates) or transient failures. Skips videos and rows already marked not_asset. Processes up to 24 assets per call — if more remain, the response returns { remaining > 0 } and the caller can invoke again. Paid (batched vision tag credit, typically < $0.01 per invocation). Input parameters: - `brand_id` (string, required): Brand whose unreviewed assets should be re-run through the vision tagger. Only assets with vision_classified=false are processed. Get from list_brands. ### `list_brand_documents` (~306 tokens) List Brand Documents List indexed brand documents for a brand. Each row carries the indexed signals (doc_type, summary, key_topics, classification_confidence, indexing_status) plus mime_type and size_bytes from the underlying file. Filter by doc_type (one of 19 values incl. voice_tone_doc, brand_guidelines, strategy_memo, customer_interview, pitch_deck, general_reference) or by indexing_status (pending, running, indexed, error). Use BEFORE read_brand_document to discover what context exists for a brand without paying the read cost. Free, read-only. Paginated via cursor. Input parameters: - `brand_id` (string, required): Brand to list documents for. Get from list_brands. - `cursor` (string): Pagination cursor returned as next_cursor on the previous page (created_at ISO timestamp). - `doc_type` (string): Filter by classified document type. One of 19 values: voice_tone_doc, brand_guidelines, strategy_memo, brand_brief, pitch_deck, research_report, campaign_brief, tone_of_voice_synthesis, customer_inte… - `indexing_status` (string): Filter by indexing pipeline state. Use "indexed" to only see fully-processed docs ready to read. - `limit` (integer): Page size. Default 20, max 100. ### `read_brand_document` (~155 tokens) Read Brand Document Read one indexed brand document. Returns the indexed metadata (doc_type, summary, key_topics, entities, key_quotes) plus the document content, routed by mime: text/markdown, text/plain, text/csv are inlined as text; application/pdf and other text-shaped mimes return an Anthropic Files API file_id (attach via document source { type:"file", file_id } on the next turn); DOCX/PPTX/XLSX return a requires_code_execution marker (caller must enable code_execution_20260120 and attach via container_upload). Use AFTER list_brand_documents to pick the right document. Free, read-only. Input parameters: - `document_id` (string, required): Document to read. Get from list_brand_documents. ### `list_skills` (~313 tokens) List Skills List skills available in the Heista skill library. Returns name, description, domain (shared / image / video / research / strategy / copy / creative / generation), type (foundation / registers / models / methodologies), version, and source_folder (managed-agents / chat-agent). Returns frontmatter only — no body content (use load_skill for that). Filter by domain, type, or source_folder. Use BEFORE load_skill to discover what craft knowledge is available without paying the body-read cost. Free, read-only. Input parameters: - `domain` (string): Filter by domain. Use "all" or omit to get every skill. Available domains depend on what has been authored; current live domains include shared, image, video. Authoring will add research, strategy, c… - `source_folder` (string): Filter by source folder. "managed-agents" = fleet skills (Mastermind, Heads Of, fleet specialists). "chat-agent" = chat-surface lenses. "all" or omit to get everything. Folder is organisation only; a… - `type` (string): Filter by skill type. "foundation" = always-on craft baseline per domain. "registers" = router-table over a references/ folder (e.g. cinema-mode, lighting). "methodologies" = how-to skills for specif… ### `load_skill` (~159 tokens) Load Skill Load the full SKILL.md body for one skill by canonical dot-notation name (e.g. "research.foundation", "research.methodologies.desk-synthesis", "shared.registers.cinema-mode"). Returns frontmatter + body + content_hash. Verifies content_hash against the registry and surfaces drift if the registry is out of sync with disk. Use AFTER list_skills to pick the right skill. For register-type skills with references/ folders, follow with load_skill_reference to pull specific references. Free, read-only. Input parameters: - `name` (string, required): Canonical skill name in dot-notation (e.g. "research.foundation", "research.methodologies.desk-synthesis", "shared.registers.cinema-mode"). Find via list_skills first. ### `load_skill_reference` (~183 tokens) Load Skill Reference Load one reference file from a register-type skill's references/ folder (e.g. "m1-narrative.md" from "shared.registers.cinema-mode"). Only register-type skills have references/ — foundations and methodologies are inline content only. Find valid reference filenames in the parent SKILL.md's router table. Path-traversal protected. Free, read-only. Input parameters: - `reference_path` (string, required): Reference filename within the skill's references/ folder (e.g. "m1-narrative.md"). Find via the parent SKILL.md router table. Path-traversal protected — must be a plain filename, no "..", no absolute… - `skill_name` (string, required): Parent skill name in dot-notation (e.g. "shared.registers.cinema-mode"). Only register-type skills have references/; foundations and methodologies are inline content only. ### `perplexity_search` (~373 tokens) Perplexity Sonar Pro Search Web-grounded search via Perplexity Sonar Pro. Returns synthesized answer text plus a structured sources[] array (url + title) the caller can evaluate per the research.foundation four-tier source ladder. Optional recency_filter (hour/day/week/month/year) for fast-decay topics. Optional search_domain_filter (up to 10 domains) for triangulating against known-authoritative sources. Use this whenever a specialist needs current, web-grounded information — landscape scans, trend research, evidence queries, counter-evidence checks, named-entity lookups. Pair with the research.foundation skill (always-on craft baseline) and the research.methodologies.desk-synthesis skill (6-phase workflow) for production-grade output. The agent decomposes the brief into sub-questions BEFORE calling this — one focused query per call, not a multi-question batch. Cost is real (~$0.005-0.015 per query); the agent should budget calls per research.foundation §6 (fact-check 1-3, single comparison 3-8, landscape scan 8-20). Input parameters: - `query` (string, required): Search query. Phrase as a natural-language question or precise topic description. The research agent has already done question decomposition — this is one focused query, not a multi-question batch. - `recency_filter` (string): Limit results to content published within this window. Use for fast-decay topics (model capabilities, platform algo changes, ad-format performance) per research.foundation §5. Omit for slow-decay top… - `search_domain_filter` (array): Restrict search to these domains (e.g. ["motionapp.com", "about.fb.com"]). Use when triangulating against known-authoritative sources, or when evidence-querying for a specific named brand/publication. ### `search` (~278 tokens) Web Search (general) General-purpose web grounding via parallel.ai (Vercel AI Gateway). Returns synthesized text excerpts plus structured sources[] with direct URLs. Use for: topic landscapes, entity-deep teardowns, recency-sharp queries, named-vendor lookups, general fact retrieval. NOT for: Reddit/X/community discourse → use search_community. NOT for: numerical effect sizes or methodology-heavy fact-check → use search_research. The agent decomposes the brief into sub-questions BEFORE calling — one focused query per call. Optional after_date (ISO YYYY-MM-DD) for fast-decay topics. Optional max_results 1-20, default 10. Input parameters: - `after_date` (string): ISO date (YYYY-MM-DD). Restrict results to content published after this date. Use for fast-decay topics (model capabilities, platform algo changes, ad-format performance) per research.foundation §5.… - `max_results` (integer): Maximum number of results to return. Default 10. Higher counts return more sources but cost more in tokens — keep at 10 for general use. - `query` (string, required): Search query. Phrase as a natural-language question or precise topic description. The research agent has already done question decomposition — this is one focused query, not a multi-question batch. ### `search_community` (~316 tokens) Community Search (Reddit / X / Substack / LinkedIn) Community-discourse search via parallel.ai with optional platform filtering. Returns synthesized text excerpts plus direct URLs to real Reddit threads, X posts from named operators, Substack essays, LinkedIn posts, Facebook posts. Use for: "what are practitioners saying about X", recurring themes in founder voice, multi-platform discourse mapping, verbatim quotes from named individuals. Per Phase 3.5 empirical A/B (Docs/solutions/architecture-decisions/search-backend-architecture-jun04.md): this tool SOLVES the Reddit/X retrieval gap that perplexity_search fundamentally couldn't fill. Optional platforms[] to restrict (e.g. ["reddit","x","substack"]). Per social-listening-synthesis §3 sample ≥3 platforms per brief. Input parameters: - `after_date` (string): ISO date (YYYY-MM-DD). Restrict to content after this date. Use for recency-sharp community signal mapping. - `max_results` (integer): Maximum results. Default 10. - `platforms` (array): Limit search to these platforms. Use ["reddit"] for r/* threads, ["x","twitter"] for X posts, ["substack"] for named essays. Omit to let the search engine choose. Per social-listening-synthesis §3 sa… - `query` (string, required): Search query. Phrase as natural-language. Focused on what people are SAYING — practitioner voice, named-operator discourse, community reaction. Not a general fact-check query. ### `search_research` (~255 tokens) Research Search (structured fact-check + numerical) Structured fact-check + numerical research via Perplexity Sonar Reasoning Pro (Gateway-routed). Returns synthesized answer text plus structured sources[] with direct URLs to primary sources. Use for: specific numerical claims with methodology context, fact-check against primary sources, effect sizes + confidence intervals, earnings transcripts / SEC filings / research papers. Per Phase 3.5 empirical A/B: 2-3× cheaper than sonar-pro with comparable or better quality on structured research. Real Meta IR press releases + earnings transcripts on Desk. 17 cites on Quant. NOT for: Reddit/X/community → use search_community. NOT for: broad topic landscapes → use search. Input parameters: - `query` (string, required): Research query. Phrase as a precise factual question — what number, what claim, what methodology. The research agent has already decomposed the brief; this is one focused query. - `recency_filter` (string): Limit results to content published within this window. Use for recent earnings, recent regulatory filings, recent industry reports. - `search_domain_filter` (array): Restrict to these domains (e.g. ["investor.atmeta.com", "sec.gov"]). Use when triangulating against known T1 sources or specific authoritative publications. ### `fetch_url` (~218 tokens) Fetch URL Drill into a specific URL after search surfaces it. Returns the extracted text content plus metadata. Internal routing: PDFs hit Anthropic Files API for OCR + structured extraction; HTML pages are fetched + text-extracted via readability-style stripping. Use for: verifying a verbatim quote from a Reddit thread, reading a primary source in full (earnings transcript, research paper), drilling into a vendor product page after search surfaced the URL. NOT for: discovering new URLs — use search/search_community/search_research first. This tool takes a known URL only. Optional max_chars 100-50000, default 8000. SSRF-protected: private IPs + localhost blocked. Input parameters: - `max_chars` (integer): Maximum characters of extracted content to return. Default 8000. Higher returns more text but costs more in agent tokens. - `url` (string, required): Absolute URL to fetch. Used to drill into a specific source after search surfaces it. Must be http:// or https://. Private IPs and localhost are blocked (SSRF protection). ### `dispatch_desk_researcher` (~338 tokens) Dispatch — desk-researcher Dispatch to the DESK RESEARCHER — source-grounded synthesis on a topic landscape. Use for: "what is known about X / give me the landscape of Y / fact-check Z / synthesize the published evidence on W". Multi-source FACT/INFERENCE extraction with citation discipline. Vertical and geography agnostic. Returns: BRIEF restatement + NOT IN SCOPE + findings with FACT/INFERENCE/SPECULATION labels + [n] citations + Sources block. NOT for: trajectory questions (use dispatch_trend_researcher) / entity teardowns (use dispatch_market_analyst) / numerical effect sizes (use dispatch_quantitative_researcher) / community quotes (use dispatch_qualitative_researcher). Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable the specialist must return. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly (parent may summarize otherwise). - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model (per _config/model-assignments.ts). - `tool_guidance` (string, required): How the specialist should approach this — which of its tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_trend_researcher` (~335 tokens) Dispatch — trend-researcher Dispatch to the TREND RESEARCHER — recency-dominant trajectory investigation. Use for: "is X a real trend / what is happening with X right now / where is X headed / what is driving X". Distinguishes trend from spike, signal from noise, real shift from echo chamber. Commits to falsifying conditions before searching. Returns: 4-axis Trend assessment (Reality / Magnitude / Direction / Horizon) + Current state + Baseline + trajectory + Drivers + Counter-signals + Sources. NOT for: static landscape questions (use dispatch_desk_researcher) / entity teardowns (use dispatch_market_analyst) / numerical analysis (use dispatch_quantitative_researcher). Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable the specialist must return. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly (parent may summarize otherwise). - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model (per _config/model-assignments.ts). - `tool_guidance` (string, required): How the specialist should approach this — which of its tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_market_analyst` (~322 tokens) Dispatch — market-analyst Dispatch to the MARKET ANALYST — entity-deep teardown of a named brand or vendor. Use for: "what is brand X / how does company Y work / decode competitor Z / teardown vendor W". Multi-axis extraction grounded in multi-class sourcing, plus defensible MOAT and credible GAP theses. Vertical and geography agnostic. Returns: 8-axis extraction (positioning / offer / audience / voice / pricing / distribution / proof / trajectory) + MOAT thesis + GAP thesis + Sources. NOT for: topic landscapes without a named entity (use dispatch_desk_researcher) / trajectory questions about a category (use dispatch_trend_researcher). Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable the specialist must return. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly (parent may summarize otherwise). - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model (per _config/model-assignments.ts). - `tool_guidance` (string, required): How the specialist should approach this — which of its tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_quantitative_researcher` (~339 tokens) Dispatch — quantitative-researcher Dispatch to the QUANTITATIVE RESEARCHER — numerical analysis with full methodology context. Use for: briefs that turn on numbers done rigorously — "what is the documented effect size of X / what does the data say about Y / quantify the impact of Z". Every load-bearing number carries sample frame, sample size, measurement instrument, time window. Often answers with insufficient-evidence when underlying data is thin (negative findings are deliverable). Returns: 4-axis Quantitative summary (Value / Methodology rigor / Effect size / Robustness) + Numerical findings table + Methodology gaps + Sources. NOT for: topic landscapes (use dispatch_desk_researcher) / community language patterns (use dispatch_qualitative_researcher). Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable the specialist must return. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly (parent may summarize otherwise). - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model (per _config/model-assignments.ts). - `tool_guidance` (string, required): How the specialist should approach this — which of its tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_qualitative_researcher` (~360 tokens) Dispatch — qualitative-researcher Dispatch to the QUALITATIVE RESEARCHER — thematic synthesis from unstructured text (interviews, reviews, forum threads, customer language). Use for: "what are the 2-3 recurring themes in how D2C founders talk about X / what language is being used around Y / what are the patterns in customer reviews of Z". Every theme carries evidence count, triangulation status, ≥1 verbatim quote, outlier-check note. SOLVES the Reddit/X/Substack named-operator voice retrieval gap that legacy search tools could not fill. Returns: Corpus + Sampling + Coding methodology + 4-axis Themes table + Theme synthesis + Outlier voices + Saturation assessment + Sources. NOT for: quantitative effect sizes (use dispatch_quantitative_researcher) / multi-platform discourse mapping (use dispatch_social_listening_researcher). Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable the specialist must return. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly (parent may summarize otherwise). - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model (per _config/model-assignments.ts). - `tool_guidance` (string, required): How the specialist should approach this — which of its tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_social_listening_researcher` (~333 tokens) Dispatch — social-listening-researcher Dispatch to the SOCIAL LISTENING RESEARCHER — multi-platform community-signal interpretation. Use for: "what are practitioners saying about X across platforms / what jargon is emerging in field Y / what is the cross-platform discourse around brand/topic Z". Treats T3 community sources as primary data, distinguishes cross-platform patterns from single-platform noise. ≥3 platforms sampled per brief. Returns: Signal map (Signal / Platforms / Volume / Sentiment + recency) + Per-platform evidence trail + Cross-platform vs single-platform classification + Confidence flag + Sources. NOT for: single-source thematic work (use dispatch_qualitative_researcher) / numerical sentiment effect sizes (use dispatch_quantitative_researcher). Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable the specialist must return. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly (parent may summarize otherwise). - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model (per _config/model-assignments.ts). - `tool_guidance` (string, required): How the specialist should approach this — which of its tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_desk_researcher_async` (~413 tokens) Dispatch (async) — desk-researcher Dispatch to the DESK RESEARCHER — source-grounded synthesis on a topic landscape. Use for: "what is known about X / give me the landscape of Y / fact-check Z / synthesize the published evidence on W". Multi-source FACT/INFERENCE extraction with citation discipline. Vertical and geography agnostic. Returns: BRIEF restatement + NOT IN SCOPE + findings with FACT/INFERENCE/SPECULATION labels + [n] citations + Sources block. NOT for: trajectory questions (use dispatch_trend_researcher) / entity teardowns (use dispatch_market_analyst) / numerical effect sizes (use dispatch_quantitative_researcher) / community quotes (use dispatch_qualitative_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs. Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly. - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model. - `tool_guidance` (string, required): How the specialist should approach this — which tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_trend_researcher_async` (~410 tokens) Dispatch (async) — trend-researcher Dispatch to the TREND RESEARCHER — recency-dominant trajectory investigation. Use for: "is X a real trend / what is happening with X right now / where is X headed / what is driving X". Distinguishes trend from spike, signal from noise, real shift from echo chamber. Commits to falsifying conditions before searching. Returns: 4-axis Trend assessment (Reality / Magnitude / Direction / Horizon) + Current state + Baseline + trajectory + Drivers + Counter-signals + Sources. NOT for: static landscape questions (use dispatch_desk_researcher) / entity teardowns (use dispatch_market_analyst) / numerical analysis (use dispatch_quantitative_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs. Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly. - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model. - `tool_guidance` (string, required): How the specialist should approach this — which tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_market_analyst_async` (~397 tokens) Dispatch (async) — market-analyst Dispatch to the MARKET ANALYST — entity-deep teardown of a named brand or vendor. Use for: "what is brand X / how does company Y work / decode competitor Z / teardown vendor W". Multi-axis extraction grounded in multi-class sourcing, plus defensible MOAT and credible GAP theses. Vertical and geography agnostic. Returns: 8-axis extraction (positioning / offer / audience / voice / pricing / distribution / proof / trajectory) + MOAT thesis + GAP thesis + Sources. NOT for: topic landscapes without a named entity (use dispatch_desk_researcher) / trajectory questions about a category (use dispatch_trend_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs. Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly. - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model. - `tool_guidance` (string, required): How the specialist should approach this — which tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_quantitative_researcher_async` (~414 tokens) Dispatch (async) — quantitative-researcher Dispatch to the QUANTITATIVE RESEARCHER — numerical analysis with full methodology context. Use for: briefs that turn on numbers done rigorously — "what is the documented effect size of X / what does the data say about Y / quantify the impact of Z". Every load-bearing number carries sample frame, sample size, measurement instrument, time window. Often answers with insufficient-evidence when underlying data is thin (negative findings are deliverable). Returns: 4-axis Quantitative summary (Value / Methodology rigor / Effect size / Robustness) + Numerical findings table + Methodology gaps + Sources. NOT for: topic landscapes (use dispatch_desk_researcher) / community language patterns (use dispatch_qualitative_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs. Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly. - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model. - `tool_guidance` (string, required): How the specialist should approach this — which tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_qualitative_researcher_async` (~435 tokens) Dispatch (async) — qualitative-researcher Dispatch to the QUALITATIVE RESEARCHER — thematic synthesis from unstructured text (interviews, reviews, forum threads, customer language). Use for: "what are the 2-3 recurring themes in how D2C founders talk about X / what language is being used around Y / what are the patterns in customer reviews of Z". Every theme carries evidence count, triangulation status, ≥1 verbatim quote, outlier-check note. SOLVES the Reddit/X/Substack named-operator voice retrieval gap that legacy search tools could not fill. Returns: Corpus + Sampling + Coding methodology + 4-axis Themes table + Theme synthesis + Outlier voices + Saturation assessment + Sources. NOT for: quantitative effect sizes (use dispatch_quantitative_researcher) / multi-platform discourse mapping (use dispatch_social_listening_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs. Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly. - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model. - `tool_guidance` (string, required): How the specialist should approach this — which tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_social_listening_researcher_async` (~408 tokens) Dispatch (async) — social-listening-researcher Dispatch to the SOCIAL LISTENING RESEARCHER — multi-platform community-signal interpretation. Use for: "what are practitioners saying about X across platforms / what jargon is emerging in field Y / what is the cross-platform discourse around brand/topic Z". Treats T3 community sources as primary data, distinguishes cross-platform patterns from single-platform noise. ≥3 platforms sampled per brief. Returns: Signal map (Signal / Platforms / Volume / Sentiment + recency) + Per-platform evidence trail + Cross-platform vs single-platform classification + Confidence flag + Sources. NOT for: single-source thematic work (use dispatch_qualitative_researcher) / numerical sentiment effect sizes (use dispatch_quantitative_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs. Input parameters: - `boundaries` (string, required): In scope vs out of scope. Explicit OUT_OF_SCOPE clauses. Constraints (e.g. "do not spawn further subagents", "only Meta paid social"). - `objective` (string, required): One sentence stating what "done" looks like — the specific deliverable. From the four-part delegation contract (agent-authoring §5). - `output_format` (string, required): The shape the specialist must return — schema, template, or specific format. If verbatim-return needed, say so explicitly. - `priority` (string): standard (default) uses the specialist's production model; deep uses its escalation model. - `tool_guidance` (string, required): How the specialist should approach this — which tools to favor, effort budget in tool calls, query angles to prioritize. ### `dispatch_head_of_research` (~343 tokens) Dispatch — Head of Research Run a full research workflow via the Head of Research agent. The Head decomposes your brief into specialist sub-questions, dispatches the right combination of 6 specialists (desk, trend, market, quant, qual, social) in parallel via async dispatch, polls them to completion, judges output quality, and returns a structured synthesis. Use for: any source-grounded research request — fact-checking, vendor teardowns, trend assessment, quantitative effect-size analysis, qualitative theme extraction, cross-platform discourse mapping, or any combination. Wall time: 2-5 min typical. Returns: { synthesis, head_session_id, status, event_count, tool_uses, elapsed_ms }. NOT for: non-research requests (writing, coding, casual chat) — respond directly without calling this. Cost: $0.20-1.50 per call depending on brief complexity (specialist token spend + Anthropic session-runtime at $0.08/hr). Input parameters: - `brief` (string, required): The research brief to send to the Head. Must be self-contained — the Head sees only this string, no conversation history. Include entity, time window, scope, and unit of analysis explicitly. Be concr… - `max_wait_seconds` (integer): Hard cap on how long to wait for the Head session to complete. Default 270 (4.5 min). Heads typically complete in 2-5 min; raise this if you expect a deep research brief. - `priority` (string): standard (default) uses production models in specialists; deep escalates to higher-capability models. Use deep when accuracy matters more than cost. ### `get_dispatch_result` (~158 tokens) Get Dispatch Result Get the current status of a specialist dispatch job started via dispatch__async. Returns { status: queued|running|completed|failed, result_text?, error_text?, error_class?, retry_count, elapsed_seconds, wait_ms_hint }. Call this repeatedly after a dispatch_*_async returns a job_id. Sleep wait_ms_hint milliseconds between calls. When status === "completed", read result_text as the specialist's full synthesis. When status === "failed", error_class tells you whether to retry (transient/scope/routing) or give up and synthesize around (permanent) per the fleet resilience pattern. Input parameters: - `job_id` (string, required): The job_id returned by a previous dispatch__async call. ### `get_fleet_cost` (~207 tokens) Get Fleet Cost Breakdown Read-only walk of a fleet session tree. Given any session_id in the tree (root, Head, Mastermind, or specialist sub-node) returns the full breakdown: every session row with depth + parent + agent_kind + node_label, the cost_events recorded against each, per-node self_cost_cents, total raw compute, tier markup estimate, and (after close_session_tree has run) the authoritative credits_charged + credits_refunded. Org-scoped: only sessions belonging to your org return data. Free — no compute cost. Use to render cost breakdown UIs, audit fleet spend, or verify a session's tree topology. Input parameters: - `root_session_id` (string, required): A session_id from anywhere in the fleet tree — root, Head, Mastermind, or specialist sub-node. The handler resolves to the actual root and walks the full subtree, so you do not need to know the root… ### `list_saved_assets` (~463 tokens) List Saved Assets List saved assets in the workspace. Filter by category (STRATEGY, IDEAS, COPY, VISUALS, MOTION, BRIEFS), by one or more formats inside the category (e.g. COPY + formats=["ad-script","hook"]), by tags (any/all), by brand_id, by brief_id (PowerSource), by created_by ("me" resolves to caller via OAuth), or favorites_only. Returns the unified view that backs the /assets page — BRIEFS rows come from creator_briefs with share URLs; other categories come from saved_assets. Use BEFORE asking the user what to pull into a Heist. Free, read-only, paginated. Input parameters: - `brand_id` (string): Limit to one brand. - `brief_id` (string): Limit to one PowerSource (brief). - `category` (string): Tab filter. STRATEGY (positioning, brand platform), IDEAS (hooks, concepts, territories), COPY (ad scripts, hooks, campaign copy), VISUALS (static ads, product, lifestyle imagery), MOTION (talking he… - `created_by` (string): User id or the literal "me". When called via OAuth, "me" resolves to the caller. API-key callers MUST pass an explicit user id (no caller identity). - `cursor` (string): Pagination cursor returned as next_cursor on the previous page. - `favorites_only` (boolean): Restrict to favorited assets only. - `formats` (array): Multi-select format pills within a category. e.g. for COPY: ["ad-script","hook"]. See save_asset description for the full per-category enum. - `limit` (integer): Page size. Default 50, max 200. - `search` (string): Full-text search on title + body + tags. - `tags` (array): Tag filter. Default mode is `any` (OR). Switch to `all` with tags_mode. - `tags_mode` (string): How to combine tags. Default `any` (overlap). `all` requires every tag. ### `get_saved_asset` (~83 tokens) Get Saved Asset Fetch one saved asset by id. Returns the full row including category, format, tags, body_text/html, signed media_url (if private storage), metadata, creator, brand, and timestamps. Use AFTER list_saved_assets to load the full record when the list projection is too sparse. Input parameters: - `asset_id` (string, required): Asset to fetch. Get from list_saved_assets. ### `get_saved_assets_batch` (~134 tokens) Get Saved Assets (Batch) Fetch up to 50 saved assets by id in one round-trip. Use when an agent needs to pull a pre-selected set — e.g. resolving a saved_asset_picker context input on a Heist that requires N pinned assets. Missing or cross-workspace ids are silently dropped; compare returned items vs requested ids to detect drops. Input parameters: - `asset_ids` (array, required): Up to 50 asset ids to fetch in one round-trip. Use when an agent needs to pull a pre-selected set of saves (e.g. resolving a saved_asset_picker declaration in a Heist). Missing/cross-workspace ids ar… ### `save_asset` (~484 tokens) Save Asset Persist a new saved asset to the workspace. category MUST be one of STRATEGY, IDEAS, COPY, VISUALS, MOTION (BRIEFS lives in creator_briefs and is not saveable through this tool). format MUST match the per-category enum (see input description). title is required. body_text + metadata are recommended. Source attribution (heist_slug, session_id, pattern) lets the user trace the save back to its origin in the /assets timeline. Brand and brief_id link the save to a PowerSource for downstream filtering. Returns the inserted asset row. Input parameters: - `body_html` (string): Rich-text body where the asset carries structured markup. - `body_text` (string): Primary text body for STRATEGY / IDEAS / COPY saves. - `brand_id` (string): Brand the asset is for. Resolved from brief_id when omitted. - `brief_id` (string): PowerSource (brief) the asset was generated against. - `category` (string, required): One of STRATEGY, IDEAS, COPY, VISUALS, MOTION. BRIEFS is not accepted — briefs live in the creator_briefs table via the briefs API. - `format` (string, required): Per-category format. STRATEGY: positioning, brand-platform, campaign-strategy, strategic-doc. IDEAS: hook, concept, big-idea, territory. COPY: ad-script, hook, campaign-copy. VISUALS: static-ad, prod… - `media_storage_path` (string): Storage path inside the private `saved-assets` bucket. Obtain via the /api/saved-assets/upload-url route (REST only; no MCP upload tool yet). - `media_url` (string): Direct media URL for VISUALS / MOTION (external host). - `metadata` (object): Type-specific bag — duration, image dimensions, model used, beat count, etc. - `parent_id` (string): Parent asset id for variations grouped under one save set. - `source` (object, required): Save attribution. - `tags` (array): Free-form tags for filtering and search. - `thumb_url` (string): Card thumbnail URL. - `title` (string, required): Card title — what shows in /assets. ### `delete_saved_asset` (~101 tokens) Delete Saved Asset Delete one saved asset by id. Destructive — confirm with the user before calling. OAuth callers can only delete saves they created themselves (Linear model — see /assets UI for org-admin override). API-key callers are treated as org-trusted and can delete on behalf of any creator in the workspace. Cleans up the storage object for private VISUALS/MOTION saves. Input parameters: - `asset_id` (string, required): Asset to delete. Get from list_saved_assets. ### `favorite_saved_asset` (~95 tokens) Favorite Saved Asset Toggle the favorite flag on a saved asset. Pass is_favorite=true to favorite, false to unfavorite. favorited_at is set/cleared in lockstep so the Favorites tab sorts correctly. Not destructive. Input parameters: - `asset_id` (string, required): Asset to favorite or unfavorite. - `is_favorite` (boolean, required): true to favorite, false to unfavorite. favorited_at is set/cleared in lockstep. ### `list_strategy_audiences` (~143 tokens) List Strategy Audiences List audience archetypes for a strategy (PowerSource). Returns the Buyer Decoder archetype (source="buyer_profile", one entry max) plus up to 3 offering primary_audience segments (source="primary_audience"). Use this to pick which audience to target before generating copy / scripts / hooks — the UI picker reads the same projection. Distinct from list_strategies (which lists scans for a brand): this lists audiences INSIDE one strategy. Input parameters: - `powersource_id` (string, required): Strategy (PowerSource / brief) id. Get from list_strategies. Returns the buyer-decoder archetype plus up to 3 primary_audience segments. ### `get_strategy` (~270 tokens) Get Strategy Read a creative strategy in full by its powersource_id. Returns the same brand-merged bundle shape as get_powersource(data) — buyer profile, 12 behavioral tensions, angles, narrative direction, tone of voice, selling points, CTAs, proof, brand story, homepage data, offering — projected through the public PowerSource API serializer. Use this when you already have a powersource_id (from list_strategies) and want the full strategy payload in one call, without the job_id round-trip that get_powersource needs. Archived strategies are excluded by default (parity with list_strategies). Pass include_archived=true to read archived strategies. Read-only, free, account-scoped. Input parameters: - `include_archived` (boolean): Include archived strategies. Default false (archived strategies are excluded from agent reads — same default as list_strategies). - `powersource_id` (string, required): Strategy (PowerSource / brief) id. Get from list_strategies. Returns the full brand-merged bundle — buyer profile, 12 behavioral tensions, angles, narrative, tone of voice, selling points, CTAs, proo… ### `list_strategy_tones` (~131 tokens) List Strategy Tones List tone profiles for a strategy. Today returns at most one entry — the tone_of_voice synthesized by the Tone of Voice Synthesis agent (POWER-mode bundles only). The shape is list-stable so future multi-tone bundles plug in without changing the contract. Use this to align generation with the brand-tied voice DNA before writing copy, hooks, or scripts. Input parameters: - `powersource_id` (string, required): Strategy (PowerSource / brief) id. Get from list_strategies. Returns the synthesized tone-of-voice (at most one entry today; shape is list-stable for future multi-tone bundles). ### `list_visual_style_presets` (~221 tokens) List Visual styles Saved style configs picked into image-led Heists. Workspace = org-owned styles. Official = canonical Heista catalog (org_id IS NULL, is_canonical=true). Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_visual_style_preset` (~72 tokens) Get Visual style Get one visual styles preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_visual_preset_presets` (~231 tokens) List Visuals Visual presets (style reference sets) backed by visual_heists. Dual scope since 2026-07-14: workspace rows saved from the Visual Preset builder + the Heista-curated official catalog. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_visual_preset_preset` (~72 tokens) Get Visual Get one visuals preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_decoded_ad_presets` (~226 tokens) List Decoded ads Structural references for script-led Heists. Workspace decodes (your video_sources scans joined with their video_scan_frameworks) + Heista-curated decoded ads from official_ad_heists. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_decoded_ad_preset` (~73 tokens) Get Decoded ad Get one decoded ads preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_ad_formula_presets` (~208 tokens) List Ad formulas Cluster-level structural formulas derived from decoded ads. Heista-curated; served as a generation parameter. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_ad_formula_preset` (~72 tokens) Get Ad formula Get one ad formulas preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_image_ad_scan_presets` (~211 tokens) List Static ads Static-ad references for image-led Heists. Workspace static scans + Heista-curated image ad heists. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_image_ad_scan_preset` (~73 tokens) Get Static ad Get one static ads preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_saved_visual_idea_presets` (~201 tokens) List Saved visual ideas Visual ideas you saved from prior generations. Workspace-only. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_saved_visual_idea_preset` (~75 tokens) Get Saved visual idea Get one saved visual ideas preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_creative_agent_presets` (~235 tokens) List Creative agents Reusable creative agents the Heist can pick as a handoff target — picked from the UI, callable as an MCP tool from Managed Agents. Workspace = private agents in the org. Official = public_template agents in any org. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_creative_agent_preset` (~73 tokens) Get Creative agent Get one creative agents preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_creative_agent_skill_presets` (~267 tokens) List Creative agent skills Portable craft skills (frameworks + method + worked examples) a Creative Agent loads ON TOP of its worldview — additive and stackable, never substitutive (unlike a creative_director_playbook, which replaces the agent for a session). Pinned per character on creative_agent_versions.skill_ids. Workspace = org-authored private skills; official = the Heista-curated starter library. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_creative_agent_skill_preset` (~75 tokens) Get Creative agent skill Get one creative agent skills preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_creative_director_playbook_presets` (~255 tokens) List Creative Director playbooks Seven-section creative-mechanism lenses the Creative Director chat picks at session start. The picked playbook substitutes Layers 3 + 4 of the system prompt — voice + foundation — for the session (the lens IS who the agent is). Workspace = private playbooks; official = the Heista-curated catalog. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_creative_director_playbook_preset` (~78 tokens) Get Creative Director playbook Get one creative director playbooks preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_talent_model_presets` (~252 tokens) List Models Saved casting talent — a person you can re-use across Heists. The Models Heist saves them on click; future Heists can pick one as a brand-aware talent reference. Workspace = your saved castings. Official = Heista-curated drops across fashion, lifestyle, everyday, character, and creator buckets. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_talent_model_preset` (~72 tokens) Get Model Get one models preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_outfit_presets` (~289 tokens) List Outfits Styled outfits — a talent dressed in a full look, saved as one composite sheet (turnaround + wardrobe detail crops) with structured refs to the product images that built it. The Outfits Heist saves them on click; future image/video Heists pick one to lock model + wardrobe in a single pick. Workspace = your saved outfits. Official = Heista-curated drops across casual, streetwear, activewear, business, evening, swim & resort, loungewear, and outerwear. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_outfit_preset` (~71 tokens) Get Outfit Get one outfits preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `list_cd_card_bookmark_presets` (~262 tokens) List Saved cards Cards the user bookmarked from Creative Director chat — directions, concepts, executions, brand platforms, art directions, visual sets. Surfaces in /library + the chat-side tray. Saves happen through the dedicated /api/creative-director/bookmarks route (NOT through /api/library), so is_savable is false here — the library surface is read-only. Read-only, free. Filter scope with only_workspace / only_official (mutually exclusive — same toggle as the in-app library lens). Page with limit + offset. Input parameters: - `brand_id` (string): Optional brand_id to scope workspace presets to. Get from list_brands. Official presets are not brand-scoped and are unaffected by this filter. - `limit` (integer): Page size. Default 24, max 100. - `offset` (integer): Offset for paging through results. Default 0. - `only_official` (boolean): When true, hide workspace (user-created) presets and only return Heista-curated (official) presets. Mutually exclusive with only_workspace. - `only_workspace` (boolean): When true, hide Heista-curated (official) presets and only return workspace (user-created) presets. Mutually exclusive with only_official. ### `get_cd_card_bookmark_preset` (~74 tokens) Get Saved card Get one saved cards preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only. Input parameters: - `id` (string, required): Preset id. Discover ids by calling the matching list__presets first. ### `fleet_intel_stats` (~88 tokens) Fleet — Canonical Corpus Stats Canonical Ad Intelligence corpus counts — the single source of truth that kills number drift across marketing surfaces. Returns decoded ads published (THE number to quote publicly), total corpus size, live brand/category/weekly report counts, live categories and verticals, and the last publish timestamp. Use this BEFORE citing any corpus number in content, outreach, or briefs. Free, read-only, no parameters. ### `fleet_search_decoded_ads` (~196 tokens) Fleet — Search Decoded Ads Search the published Ad Intelligence corpus (the public decode gallery). Filter by free-text (name/tagline/brand), brand, category, vertical, or platform. Returns list rows with public URLs — never the full structural payload (use fleet_get_decoded_ad for that). Hard cap 50 rows per call; paginate with offset. Read-only. Input parameters: - `brand` (string): Filter by brand name (partial match). - `category` (string): Filter by gallery category (exact match — values from fleet_intel_stats / prior searches). - `limit` (integer): Max rows (default 10, hard cap 50). - `offset` (integer): Pagination offset. - `platform` (string): Filter by platform (e.g. facebook, tiktok). - `query` (string): Free-text match against ad name, tagline, and brand name. - `vertical` (string): Filter by vertical classification (exact match). ### `fleet_get_decoded_ad` (~98 tokens) Fleet — Get Decoded Ad Read one published decode in full by id or slug, including its public structural payload (beats, classification, patterns — the same data rendered on the public decode page). Use for proof points, content briefs, and pattern citations. Not for customer workspace decodes — only the published corpus. Read-only. Input parameters: - `id_or_slug` (string, required): Decode id (uuid) or public slug — both appear in fleet_search_decoded_ads results. ### `fleet_list_brand_reports` (~103 tokens) Fleet — List Brand Reports List live brand-level Ad Intelligence reports (the public /decode/brand pages). Optional brand-name filter, paginated, hard cap 50 rows. Returns identifiers + ad counts + public URLs; use fleet_get_brand_report for a full report. Read-only. Input parameters: - `limit` (integer): Max rows (default 10, hard cap 50). - `offset` (integer): Pagination offset. - `query` (string): Filter by brand name (partial match). ### `fleet_get_brand_report` (~57 tokens) Fleet — Get Brand Report Read one live brand report in full by slug, including the creative intelligence payload used on the public brand page — proof points for outreach and positioning. Read-only. Input parameters: - `slug` (string, required): Brand report slug from fleet_list_brand_reports. ### `fleet_list_intelligence_articles` (~118 tokens) Fleet — List Intelligence Articles List live intelligence articles — the weekly and per-vertical category report system behind the public intelligence surfaces. Filter by kind (weekly/category) or vertical. Note: individual static deep-dive articles are not DB rows and are not listed here. Hard cap 50 rows. Read-only. Input parameters: - `kind` (string): Filter by article kind. - `limit` (integer): Max rows (default 10, hard cap 50). - `offset` (integer): Pagination offset. - `vertical` (string): Filter category articles by vertical. ### `fleet_analytics_overview` (~100 tokens) Fleet — Analytics Overview Aggregate marketing analytics for the last 7/28/90 days: pageviews, visitors, sessions, AI-search-referred sessions, the view→engaged→CTA→signup→trial funnel, and top pages. Aggregates only — never person-level data. Returns an error result if product analytics is not configured. Read-only. Input parameters: - `days`: Window in days: 7, 28, or 90. Default 7. ### `fleet_analytics_top_pages` (~134 tokens) Fleet — Analytics Top Pages Top marketing pages by views for the last 7/28/90 days, optionally filtered to a path prefix (e.g. "/decode", "/intelligence", "/brands"). Includes engagement signals where captured. Aggregates only, hard cap 50 rows. Read-only. Input parameters: - `days`: Window in days: 7, 28, or 90. Default 7. - `limit` (integer): Max rows (default 20, hard cap 50). - `path_prefix` (string): Only include paths starting with this prefix (e.g. "/decode", "/intelligence", "/brands"). ### `fleet_analytics_trend` (~115 tokens) Fleet — Analytics Trend Daily pageview series for the last 7/28/90 days, split by traffic source category (ai_search / organic / social / direct / referral). Use to measure launch weeks and content momentum. Aggregates only. Read-only. Input parameters: - `days`: Window in days: 7, 28, or 90. Default 28. - `metric` (string): Metric for the daily series. v1 supports pageviews (split by source category: ai_search / organic / social / direct / referral). ### `fleet_gsc_summary` (~122 tokens) Fleet — Search Performance Summary Google Search performance totals from first-party Search Console data (synced 6-hourly): clicks, impressions, CTR, impression-weighted average position, distinct queries and pages. Optional page-path filter. Data lags real traffic by ~2-3 days; max window 28 days. Read-only. Input parameters: - `days` (integer): Window in days (max 28 — GSC data lags ~2-3 days). Default 28. - `page_prefix` (string): Only include pages whose URL contains this path (e.g. "/decode"). ### `fleet_gsc_top_queries` (~129 tokens) Fleet — Top Search Queries Top Google search queries by clicks or impressions from first-party Search Console data, optionally filtered to pages containing a path (e.g. "/decode"). The core tool for briefing programmatic SEO. Hard cap 50 rows, max window 28 days. Read-only. Input parameters: - `days` (integer): Window in days (max 28). Default 28. - `limit` (integer): Max rows (default 25, hard cap 50). - `order_by` (string): Sort key. Default clicks. - `page_prefix` (string): Only include pages whose URL contains this path. ### `fleet_gsc_top_pages` (~126 tokens) Fleet — Top Search Pages Top pages by Google search clicks or impressions from first-party Search Console data, optionally filtered to queries containing a term. Use to find which pSEO pages earn search demand. Hard cap 50 rows, max window 28 days. Read-only. Input parameters: - `days` (integer): Window in days (max 28). Default 28. - `limit` (integer): Max rows (default 25, hard cap 50). - `order_by` (string): Sort key. Default clicks. - `query_contains` (string): Only include rows whose search query contains this text. ### `fleet_gsc_query` (~281 tokens) Fleet — Search Console Live Query LIVE Google Search Analytics query — group by any dimensions (date, page, query, country, device, searchAppearance; up to 3) with page/query filters over up to 16 months of history. Richer than the snapshot tools: use this for ad-hoc analysis. NOTE: including the "query" dimension omits anonymized rare queries — use ["date"] or ["page"] for complete totals on low-traffic sites. Hard cap 100 rows. Read-only. Input parameters: - `dimensions` (array, required): Group-by dimensions, up to 3 (e.g. ["query"], ["page","query"], ["date"]). NOTE: including "query" omits anonymized/rare queries — totals with ["date"] or ["page"] are more complete on low-traffic si… - `end_date` (string): ISO date. Default: 3 days ago (GSC lags ~2-3 days). - `page_contains` (string): Filter: page URL contains this string (e.g. "/decode"). - `query_contains` (string): Filter: search query contains this string. - `row_limit` (integer): Max rows (default 25, hard cap 100). - `start_date` (string): ISO date (YYYY-MM-DD). Default: 28 days ago. GSC holds ~16 months of history. ### `fleet_gsc_inspect_url` (~123 tokens) Fleet — Inspect URL Index Status Google URL Inspection for one heista.co URL: index verdict, coverage state ("Submitted and indexed" / "Crawled - currently not indexed" / "URL is unknown to Google"), last crawl time, robots state, and canonical resolution. THE tool for diagnosing why a page has no impressions. Quota ~2,000 inspections/day — batch thoughtfully. Read-only (requesting indexing is not possible via API). Input parameters: - `url` (string, required): Full heista.co URL to inspect (e.g. "https://www.heista.co/decode"). ### `fleet_gsc_sitemaps` (~73 tokens) Fleet — Sitemap Indexing Scoreboard Sitemaps registered on the Search Console property with submitted vs indexed counts, last-download time, warnings and errors. The indexing-progress scoreboard — as of Jul 2026 the main sitemap had 2,432 submitted / 0 indexed. Track this as SEO fixes land. No parameters. Read-only. ### `fleet_crawler_hits` (~266 tokens) Fleet — AI Crawler Hits Server-logged crawler fetches: which AI engines (GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot, …) and search crawlers (Googlebot, Bingbot) fetched which heista.co pages, and when. This signal is invisible to page analytics — crawlers never run the tracking script. Group by bot, page, or date; filter by bot or path. Logging began 2026-07-23 (no earlier history exists). 180-day retention. Read-only. Input parameters: - `bot` (string): Filter to one bot (canonical names: gptbot, oai-searchbot, chatgpt-user, claudebot, claude-user, perplexitybot, perplexity-user, googlebot, bingbot, …). - `days` (integer): Window in days (default 28, max 180 — retention limit). - `group_by` (string): Aggregation: by bot (default — which engines are crawling), by page (what they fetch), or by date (crawl cadence). - `limit` (integer): Max rows (default 25, hard cap 100). - `path_contains` (string): Filter: page path contains this string (e.g. "/decode", "/intelligence"). ### `fleet_site_audit_summary` (~78 tokens) Fleet — Site Audit Summary One-row health scoreboard from the weekly full-site crawl: total pages, OK/redirect/error counts, ORPHAN pages (200 but zero internal inlinks — the primary indexing-recovery target), thin pages, missing meta/titles, total internal links, last crawl time. Start here before drilling into fleet_site_pages. Read-only. ### `fleet_site_pages` (~242 tokens) Fleet — Site Pages Inventory Filterable inventory of every sitemap-listed page with SEO facts (title, meta description, canonical, h1, word count, JSON-LD) and internal inlink/outlink counts from the weekly crawl. Filters: path_contains, orphans_only (zero inlinks), max_word_count (thin content), status, missing_meta. Sorted fewest-inlinks first — the pages Google cannot discover float to the top. Hard cap 100 rows. Read-only. Input parameters: - `limit` (integer): Max rows (default 25, hard cap 100). - `max_word_count` (integer): Only pages at or below this visible word count (thin-content filter, e.g. 150). - `missing_meta` (boolean): Only pages missing a meta description. - `offset` (integer): Pagination offset. - `orphans_only` (boolean): Only pages with ZERO internal inlinks — the pages Google has no path to discover. - `path_contains` (string): Filter: path contains this string (e.g. "/decode/", "/intelligence"). - `status` (integer): Filter by HTTP status (e.g. 200, 308, 404). ### `fleet_site_links` (~138 tokens) Fleet — Site Link Graph Internal links for one page: direction "in" = who links TO it (zero inlinks = orphan), "out" = what it links to. Link data comes from the first render of sitemap-listed pages (pagination-only links are not observed — which mirrors crawler discovery). Hard cap 200 rows. Read-only. Input parameters: - `direction` (string): "in" = pages linking TO this path (default); "out" = pages this path links to. - `limit` (integer): Max rows (default 50, hard cap 200). - `path` (string, required): Page path (e.g. "/decode/some-slug"). ### `fleet_seo_recovery` (~185 tokens) Fleet — SEO Indexing-Recovery Scorecard One-row verification scorecard for the indexing-recovery plan (internal-linking-spec-v2): site-wide + /decode-specific orphan counts (200 but zero internal inlinks) from the latest crawl, Googlebot vs other-bot crawl activity over a window (default 7 days, max 180), and Google Search Console impressions/clicks/avg-position over the trailing 28 days — each figure compared against the pre-fix baseline (1,521 orphans, 7 Googlebot pages/wk, 2 GSC impressions). Use this to confirm PR-A/B/C landed and is moving the needle, not just that the code shipped. Read-only. Input parameters: - `days` (integer): Crawler-hit window in days (default 7, max 180 — retention limit). GSC + orphan figures are always current-snapshot / trailing-28d. ### `fleet_list_issues` (~223 tokens) Fleet — List Linear Issues List issues on the Heista Linear board (team HEI) — the read-only window into what the dev agents are working on, what's broken, and what's shipped. Filter by workflow state ("Backlog"/"Todo"/"In Progress"/"In Review"/"Done"), label ("Bug"/"Security"/"SEO"/"In-App Feedback"/…), or a title search. Returns identifier + state + priority + assignee + labels; use fleet_get_issue for the full description + comments. Hard cap 100 rows. Read-only — the fleet cannot create, edit, or close tickets. Input parameters: - `label` (string): Filter by label name (e.g. "Bug", "Security", "SEO", "In-App Feedback"). - `limit` (integer): Max rows (default 30, hard cap 100). - `query` (string): Text search over issue titles (case-insensitive). - `state` (string): Filter by workflow state name: "Backlog", "Todo", "In Progress", "In Review", or "Done". ### `fleet_get_issue` (~92 tokens) Fleet — Get Linear Issue Read one Heista Linear issue in full by identifier (e.g. "HEI-14") or UUID: title, state, priority, assignee, labels, full description, and recent comments (including the automated scope/fix notes agents leave). Locked to team HEI. Read-only. Input parameters: - `identifier` (string, required): Issue identifier like "HEI-14" (preferred) or the issue UUID. ### `fleet_product_user_summary` (~138 tokens) Fleet — Customer Product Summary Look up ONE customer's product-backend state by email: trial status, credit balance, workspace plan, installed Heists, onboarding completion, plus PostHog attribution/engagement signals (source, 30d activity, page journey). This is the data PostHog structurally cannot see — whether they actually have active credits, are on a paid plan, or installed anything. Requires mcp:fleet:customer_pii (a separate, PII-adjacent scope — see Docs/systems/fleet-access.md). Every call is audit-logged. Read-only. Input parameters: - `email` (string, required): The user email to look up. ### `fleet_product_signups_recent` (~123 tokens) Fleet — Recent Signups List recent signups (last N days, hard cap 50 rows) with the same product-backend shape as fleet_product_user_summary — trial/credits/plan/onboarding/Heists per user. Use to see the newest cohort at a glance before drilling into individuals. Requires mcp:fleet:customer_pii. Every call is audit-logged. Read-only. Input parameters: - `days` (integer): Signup window in days (max 90). Default 7. - `limit` (integer): Max rows (default 20, hard cap 50). ### `fleet_product_funnel_summary` (~119 tokens) Fleet — Signup Funnel Summary Aggregate conversion/drop-off stats for a signup cohort (last N days, max 90): trial active vs expired-unconverted vs converted-to-paid, conversion rate, never-spent-a-credit rate, onboarding completion rate, and the most-installed Heists. Answers "where is the funnel leaking" in one call instead of aggregating individual summaries. Requires mcp:fleet:customer_pii. Every call is audit-logged. Read-only. Input parameters: - `days` (integer): Signup cohort window in days (max 90). Default 30. ### `creative_get_authoring_contract` (~42 tokens) Creative Library — Get Authoring Contract Read the canonical shelves, taxonomy, relationship Lego, safe content rules, and positional slots for every publishable Creative Library format. Call this before authoring. ### `creative_list_articles` (~53 tokens) Creative Library — List Articles List Creative Library drafts and publication state. Returns editorial metadata only, not customer data. Input parameters: - `limit` (integer): Page size, from 1 to 100. - `offset` (integer): Zero-based page offset. ### `creative_get_draft` (~46 tokens) Creative Library — Get Draft Read one Creative Library draft so it can be reviewed before explicit publication. Input parameters: - `shelf` (string, required): One canonical Creative Library shelf. - `slug` (string, required): The article slug. ### `creative_save_draft` (~57 tokens) Creative Library — Save Draft Validate and save a complete Creative Library draft using the canonical taxonomy and format-specific content slots. An identical retry is a no-op. It cannot replace published content or publish a page. Input parameters: - `content` (required) - `frontmatter` (required) ### `creative_publish_article` (~76 tokens) Creative Library — Publish Article Publish only the exact reviewed draft revision. If the draft changed after review, publication fails and the new revision must be reviewed. Input parameters: - `expected_revision` (string, required): The exact revision returned by creative_get_draft after human review. - `shelf` (string, required): The reviewed draft shelf. - `slug` (string, required): The reviewed draft slug. ### `creative_unpublish_article` (~70 tokens) Creative Library — Unpublish Article Take a live Creative Library article down. It stops being public and leaves the sitemap immediately. The draft is preserved and can be re-published after review. Use this to reverse a publication. Input parameters: - `shelf` (string, required): The live article shelf. - `slug` (string, required): The live article slug. ## Diagnostics Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/co-heista-api/api-mcp-mcp#diagnostics ## Score history - 2026-08-03: 76 - 2026-08-02: 76 - 2026-08-01: 75 - 2026-07-31: 75 - 2026-07-30: 68 - 2026-07-29: 69 - 2026-07-28: 68 - 2026-07-27: 68 - 2026-07-26: 67 ## Links - Remote endpoint: https://www.heista.co/api/mcp/mcp - Repository: https://github.com/Heista-co/heista-mcp - Website: https://www.heista.co/ - Changelog RSS feed: https://verifymcp.io/servers/co-heista-api/api-mcp-mcp/changelog.xml - Changelog JSON feed: https://verifymcp.io/servers/co-heista-api/api-mcp-mcp/changelog.json - HTML version of this page: https://verifymcp.io/servers/co-heista-api/api-mcp-mcp