# Edgar (remote · gateway.pipeworx.io)

EDGAR MCP — SEC EDGAR public APIs (free, no auth)

- Trust score: 59/100 (low)
- Change this week: +1
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `gateway.pipeworx.io`: 59/100 (this document), [markdown](https://verifymcp.io/servers/pipeworx-io-edgar/edgar-mcp.md), [page](https://verifymcp.io/servers/pipeworx-io-edgar/edgar-mcp)

## Channel facts

- Endpoint: `https://gateway.pipeworx.io/edgar/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.3.2`

## 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**: 46/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: this server exposes a tool marked destructive (forget) and its handshake is open, but we could not confirm whether a tool call is gated, so we do not assert it is callable unauthenticated.
  - HTTPS check failed: the endpoint is reachable over plaintext HTTP.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 73/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 14140 tokens (~328/item across 43 items; 43 tools + 0 resources), over budget; trim descriptions and params.
  - Tools include usage 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).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (12% of tools); any adoption earns full credit.
- **Capabilities**: 40/100
  - Spec-recency check failed: implements MCP spec 2025-03-26; the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http pipeworx-io-edgar https://gateway.pipeworx.io/edgar/mcp
```

### Codex

```toml
[mcp_servers.pipeworx-io-edgar]
url = "https://gateway.pipeworx.io/edgar/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "pipeworx-io-edgar": {
      "type": "remote",
      "url": "https://gateway.pipeworx.io/edgar/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add pipeworx-io-edgar --url https://gateway.pipeworx.io/edgar/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  pipeworx-io-edgar:
    url: "https://gateway.pipeworx.io/edgar/mcp"
```

### Other

```json
{
  "mcpServers": {
    "pipeworx-io-edgar": {
      "type": "http",
      "url": "https://gateway.pipeworx.io/edgar/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.

### 2026-08-03 (score 59, +1)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “ask_pipeworx” rewrote its description, which is the text the model reads
- [security] Tool “deep_research” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx_grounded” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx_beta” rewrote its description, which is the text the model reads

### 2026-08-02 (score 58, 0)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “ask_pipeworx_beta” rewrote its description, which is the text the model reads
- [security] Tool “deep_research” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx_grounded” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx” rewrote its description, which is the text the model reads

### 2026-08-01 (score 58, 0)

- [security regression] Authorization: fail → unverified
- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “ask_pipeworx” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx_beta” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx_grounded” rewrote its description, which is the text the model reads
- [security] Tool “deep_research” rewrote its description, which is the text the model reads

### 2026-07-31 (score 58, −1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 59, 0)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-29 (score 59, +1)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “deep_research” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx_grounded” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx_beta” rewrote its description, which is the text the model reads

### 2026-07-28 (score 58, 0)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “ask_pipeworx_grounded” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx_beta” rewrote its description, which is the text the model reads
- [security] Tool “ask_pipeworx” rewrote its description, which is the text the model reads
- [security] Tool “deep_research” rewrote its description, which is the text the model reads

### 2026-07-27 (score 58, 0)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

## MCP tools (43)

### `ask_pipeworx` (~396 tokens)

Ask Pipeworx

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 5,344 tools across 1393 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs.

Input parameters:

- `input` (string): Alias for question.
- `prompt` (string): Alias for question.
- `q` (string): Alias for question.
- `query` (string): Alias for question.
- `question` (string, required): Your question or request in natural language. Accepts query, q, prompt, text, input as aliases.
- `text` (string): Alias for question.

### `ask_pipeworx_beta` (~203 tokens)

Ask Pipeworx Beta

Beta version of ask_pipeworx: identical universal router (same 5,344 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so this currently matches ask_pipeworx exactly. Use it exactly like ask_pipeworx when you want the newest routing; results are compared against the stable router to decide what merges. Falls back to nothing — this IS a full working router, just the experimental edge.

Input parameters:

- `input` (string): Alias for question.
- `prompt` (string): Alias for question.
- `q` (string): Alias for question.
- `query` (string): Alias for question.
- `question` (string, required): Your question or request in natural language. Accepts query, q, prompt, text, input as aliases.
- `text` (string): Alias for question.

### `ask_pipeworx_grounded` (~268 tokens)

Ask Pipeworx — Grounded

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,344 across 1393 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input parameters:

- `input` (string): Alias for question.
- `prompt` (string): Alias for question.
- `q` (string): Alias for question.
- `query` (string): Alias for question.
- `question` (string, required): Your question in natural language. Accepts query, q, prompt, text, input as aliases.
- `text` (string): Alias for question.

### `search_within` (~238 tokens)

Search Within a Source

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input parameters:

- `limit` (number): Max passages to return (1-20, default 5).
- `query` (string, required): Natural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".
- `text` (string, required): The document text to search inside (max ~200K chars).

### `deep_research` (~530 tokens)

Deep Research

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1393 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,344 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input parameters:

- `depth` (string): How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (pa…
- `question` (string, required): The research question, in natural language. Broad/multi-part is fine — decomposition is the point.

### `discover_tools` (~260 tokens)

Discover Tools

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input parameters:

- `description` (string): Alias for query.
- `limit` (number): Maximum number of tools to return (default 20, max 50)
- `q` (string): Alias for query.
- `query` (string, required): Natural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as…
- `search` (string): Alias for query.
- `task` (string): Alias for query.

### `resolve_entity` (~253 tokens)

Resolve Entity

"What's the ticker for…" / "find the CIK for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" — resolve a user-spoken NAME to the canonical/official identifier other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (returns ticker + 10-digit CIK + company_name from SEC EDGAR + pipeworx://edgar/company/{cik} citation URI; accepts ticker, CIK, or company name as input — auto-disambiguated), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/{rxcui} citation; accepts brand or generic name). Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input parameters:

- `type` (string, required): Entity type: "company" or "drug".
- `value` (string, required): For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin").

### `compare_entities` (~264 tokens)

Compare Entities

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input parameters:

- `type` (string, required): Entity type: "company" or "drug".
- `values` (array, required): For company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

### `subscribe` (~449 tokens)

Subscribe to Alerts

Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"you@x.com"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).

Input parameters:

- `delivery` (object): Optional delivery channels in addition to the always-on persistent feed. {email:"you@x.com"} sends a templated alert per fired event. {sms:"+15551234567"} sends an SMS per event — must match the veri…
- `params` (object, required): Type-specific filter. sec_8k: {ticker:"AAPL", items?:["5.02","1.01"]}. polymarket_edge: {topic:"fed", min_spread_bps?:500}. fred_series: {series_id:"UNRATE"}. patent_grant: {applicant:"Apple Inc."}.…
- `type` (string, required): Subscription type.

### `unsubscribe` (~60 tokens)

Unsubscribe from Alerts

Cancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.

Input parameters:

- `id` (string, required): Subscription id (uuid) returned by subscribe.

### `list_subscriptions` (~72 tokens)

List Subscriptions

List the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.

Input parameters:

- `include_inactive` (boolean): Include cancelled subscriptions in the response (default false).

### `recent_alerts` (~204 tokens)

Recent Alerts

Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.

Input parameters:

- `limit` (number): Max events to return (1-200, default 50).
- `mark_read` (boolean): Flag the returned events read in the same call (default false).
- `since` (string): Optional ISO timestamp — return events fired_at >= this time.
- `type` (string): Optional — filter to one subscription type.
- `unread_only` (boolean): Return only events where read_at is null (default false).

### `entity_profile` (~318 tokens)

Entity Profile

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

Input parameters:

- `type` (string, required): Entity type. Only "company" supported today; person/place coming soon.
- `value` (string, required): Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). Names not supported — use resolve_entity first if you only have a name.

### `recent_changes` (~320 tokens)

Recent Changes

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input parameters:

- `since` (string, required): Window start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
- `type` (string, required): Entity type. Only "company" supported today.
- `value` (string, required): Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

### `validate_claim` (~306 tokens)

Validate Claim

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), the grounded or structured actual value with pipeworx:// citation, and reasoning. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input parameters:

- `claim` (string, required): Natural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
- `tolerance_pct` (number): Max percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted.…

### `scan_dependency` (~254 tokens)

Scan Dependency

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

Input parameters:

- `package` (string, required): npm package name. Scoped packages (e.g. "@types/node") are accepted.
- `version` (string): Specific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

### `bet_research` (~997 tokens)

Bet Research

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg…

Input parameters:

- `depth` (string): quick = 2-3 evidence sources, thorough = full fan-out. Default thorough.
- `include_raw` (boolean): Default false. When false (recommended), FRED/FDA/GDELT/Federal-Register evidence is summarized to the few fields agents actually use — keeps responses under ~20KB. Pass true to get full upstream pay…
- `market` (string, required): Polymarket slug ("will-bitcoin-hit-150k-by-june-30-2026"), full URL ("https://polymarket.com/event/..."), or question text ("Will Bitcoin hit $150k by June 30?")

### `polymarket_arbitrage` (~558 tokens)

Polymarket Arbitrage

Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

Input parameters:

- `event` (string): Single-event mode (use this if you know the specific Polymarket event): event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k". Full Polymarket URLs also accepted.
- `topic` (string): Cross-event mode (use this if you want to scan related events across the platform): a topic or seed question like "Fed rate decision" or "Strait of Hormuz traffic returns to normal". Tool searches Po…

### `polymarket_edges` (~1018 tokens)

Polymarket Edges

Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (after slippage), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates, knob dropped them). Cached 1h at the KV level keyed on all knobs.

Input parameters:

- `category_filter` (string): Comma-separated list to restrict the output: "model_driven" (crypto_price + news_momentum), "structural_arbitrage" (partition_overround), "concentrated_longshot". Combine like "model_driven,structura…
- `limit` (number): Top N edges to return after ranking. Default 10, max 25.
- `max_spread_pp` (number): Tradeable-edge filter. Maximum bid/ask spread in percentage points on the representative market. Default null (no filter). Set to 2 to require tight books — anything wider eats most plausible edges.
- `min_edge_pp` (number): Minimum |edge| in percentage points to include (default 0.5). Edge is evaluated NET of slippage.
- `min_kelly` (number): Minimum half-Kelly fraction (as decimal, e.g. 0.005 = 0.5% of bankroll) to include single-leg opportunities. Default 0 (no filter). Skips opportunities that are too small to bet sensibly even if the…
- `min_liquidity` (number): Tradeable-edge filter. Minimum $ liquidity on the representative market (or for partition_overround, on at least one top_leg). Default 0 (no filter). Set to 5000 to drop thin-book opportunities where…
- `min_partition_leg_kelly` (number): Minimum BEST per-leg half-Kelly fraction across a partition_overround opportunity's top_legs (or longshot_basket legs). Default 0 (no filter). Partition arbs always return kelly_fraction_half=0 at th…
- `slippage_pp` (number): Assumed execution slippage in percentage points per leg (default 0.3). Subtracted from raw |edge| before ranking and Kelly sizing. Polymarket has zero trading fees as of 2024 but bid/ask + thin depth…
- `window` (string): Polymarket volume window to filter markets. Default 1wk.

### `polymarket_kalshi_spread` (~552 tokens)

Polymarket–Kalshi Spread

Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning fires in two cases — (a) matched_pairs:0 with skipped_cross_type>0 means the venues frame the topic with non-equivalent bet shapes (e.g. Kalshi range_bucket point-in-time vs Polymarket cumulative_threshold touch-anywhere — no arb exists), (b) matched_pairs:0 with skipped_cross_type:0 and both venues >5 legs means the token-overlap matcher found nothing in common — events likely semantically unrelated despite the topic keyword. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period; aligned:false means spreads are mathematically meaningless across the temporal gap. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.

Input parameters:

- `kalshi_event_ticker` (string): Explicit Kalshi event ticker, e.g. "KXFED-26OCT". Overrides the topic-mapped Kalshi side.
- `polymarket_event_slug` (string): Explicit Polymarket event slug, e.g. "fed-decision-in-june-825". Overrides the topic-mapped Polymarket side.
- `topic` (string): Pre-mapped: fed | btc | cpi | gdp | sp500 | recession | next_pope | next_uk_pm | next_israel_pm | 2028_president

### `polymarket_fill_risk` (~479 tokens)

Polymarket Fill Risk

Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

Input parameters:

- `event` (string): Basket mode: event slug or full polymarket.com URL — checks every leg of the partition.
- `market` (string): Single-market mode: market slug or full polymarket.com URL.
- `side` (string): Single-market: buy_yes | sell_yes | buy_no | sell_no (default buy_yes). Basket: sell_yes | buy_yes (default auto — sell if partition sum > 1, buy if < 1).
- `size_usd` (number): Single-market: USD to spend (buys) or target proceeds (sells). Basket: settlement notional — shares per leg, each paying $1 at resolution. Default 1000, clamp 10–1,000,000.

### `polymarket_edge_tracker` (~329 tokens)

Polymarket Edge Tracker

Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage), not intraday.

Input parameters:

- `days` (number): Lookback in days (default 14, clamp 2-30).
- `window` (string): Which polymarket_edges window family to read snapshots for: 24hr | 1wk | 1mo (default 1wk).

### `pipeworx_trending` (~173 tokens)

Pipeworx Trending

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input parameters:

- `window` (string): 24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

### `suggest_questions` (~240 tokens)

What Can I Ask Pipeworx?

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input parameters:

- `topic` (string): Optional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

### `generate_llms_txt` (~164 tokens)

Generate llms.txt

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input parameters:

- `max_links` (number): Maximum number of link entries to include (default 25, max 50).
- `url` (string, required): Full URL of the site to summarize, e.g. "https://example.com" or a specific landing page.

### `ai_visibility_check` (~269 tokens)

AI Visibility Check

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input parameters:

- `_apiKey` (string): Optional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
- `context` (string): Optional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.
- `entity` (string, required): The thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
- `models` (array): Which models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.

### `scan_competitor_ai_presence` (~225 tokens)

Scan Competitor AI Presence

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input parameters:

- `_apiKey` (string): Optional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
- `context` (string): Optional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
- `entities` (array, required): Array of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.
- `models` (array): Which models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.

### `pipeworx_feedback` (~226 tokens)

Send Pipeworx Feedback

Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

Input parameters:

- `context` (object): Optional structured context: which tool, pack, or vertical this relates to.
- `message` (string, required): Your feedback in plain text. Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max.
- `type` (string, required): bug = something broke or returned wrong data. feature = a new tool or capability you wish existed. data_gap = data Pipeworx does not currently expose. praise = positive note. other = anything else.

### `remember` (~144 tokens)

Remember

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input parameters:

- `key` (string, required): Memory key (e.g., "subject_property", "target_ticker", "user_preference")
- `value` (string, required): Value to store (any text — findings, addresses, preferences, notes)

### `recall` (~101 tokens)

Recall

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input parameters:

- `key` (string): Memory key to retrieve (omit to list all keys)

### `forget` (~55 tokens)

Forget

Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

Input parameters:

- `key` (string, required): Memory key to delete

### `edgar_search_filings` (~345 tokens)

Edgar Search Filings

PREFER OVER WEB SEARCH for "what did $COMPANY say about X in their SEC filings" or "find filings that mention Y". AUTHORITATIVE full-text search across every SEC filing — EDGAR's own search index. Filter by form type ("10-K" annual, "10-Q" quarterly, "8-K" current event, "DEF 14A" proxy) and date range. Returns entity name, CIK, form type, filing/period dates, location, accession number (feed straight into edgar_filing_text / edgar_filing_documents — no second lookup), and — for 8-K results — the `items` array of item codes (e.g. "3.01" listing deficiency vs "1.01" material agreement vs "3.02" unregistered sale), which carry the actual signal. Use when you need to find filings matching a topic across the whole market, not for a specific company (for that use edgar_company_filings).

Input parameters:

- `end_date` (string): End date in YYYY-MM-DD format (e.g., "2024-12-31")
- `form_type` (string): Filter by SEC form type (e.g., "10-K", "10-Q", "8-K", "DEF 14A"). Omit for all types.
- `limit` (number): Number of results to return (1-40, default 10)
- `query` (string, required): Search query (e.g., "artificial intelligence", "Tesla revenue")
- `start_date` (string): Start date in YYYY-MM-DD format (e.g., "2024-01-01")

Output parameters:

- `date_range` (object)
- `form_type_filter` (string): Form type filter applied or 'all'
- `query` (string): The search query used
- `results` (array)
- `total_hits` (number): Total number of matching filings

### `edgar_company_filings` (~259 tokens)

Edgar Company Filings

AUTHORITATIVE list of recent SEC filings for a specific US public company. Pass a ticker ("AAPL") or CIK ("320193"). Filter by form type — "10-K" (annual report), "10-Q" (quarterly), "8-K" (material event — but for severity-classified 8-Ks specifically, prefer sec_8k_recent), "DEF 14A" (proxy), "S-1" (IPO registration), etc. Returns filing dates, form types, accession numbers, document links. Use for "what did $TICKER recently file" or "show me the last N proxy statements for $TICKER". For specific financial metrics over time use edgar_company_concept; for the full XBRL dump use edgar_company_facts.

Input parameters:

- `form_type` (string): Filter by SEC form type (e.g., "10-K", "10-Q", "8-K"). Omit for all types.
- `limit` (number): Max filings to return (1-40, default 20)
- `ticker_or_cik` (string, required): Ticker symbol (e.g., "AAPL") or CIK number (e.g., "320193")

Output parameters:

- `cik` (string): Company CIK number
- `company_name` (string): Official company name
- `filings` (array)
- `filter_form_type` (string): Form type filter applied or 'all'
- `fiscal_year_end` (string): Fiscal year end date
- `sic_description` (string): Standard Industrial Classification description
- `state_of_incorporation` (string): State where company is incorporated
- `tickers` (array): Associated ticker symbols

### `edgar_company_facts` (~159 tokens)

Edgar Company Facts

AUTHORITATIVE full XBRL fundamentals dump for a US public company by CIK. Returns every reported financial metric (hundreds of concepts: revenue, net income, assets, liabilities, EPS, cash flow lines, segment breakdowns) with annual and historical values pulled straight from the company's SEC filings — the official numbers, not estimates. Use when you need the complete fundamental picture vs. one metric (for one metric use edgar_company_concept). Large payload; agents typically use this once to discover available concepts then narrow to edgar_company_concept for follow-up queries.

Input parameters:

- `cik` (string, required): Ticker ("NVDA") or CIK number ("320193"). Tickers are auto-resolved to CIKs internally.

Output parameters:

- `available_concepts` (number): Total number of available US-GAAP concepts
- `cik` (string): Company CIK number
- `company_name` (string): Official company name
- `key_financials` (object): Key financial metrics with most recent annual values

### `edgar_company_concept` (~431 tokens)

Edgar Company Concept

AUTHORITATIVE historical financials for any US public company. Source: SEC XBRL filings (the official numbers companies file, not third-party scrapes). Pass a ticker or CIK plus a friendly metric name — Revenue, NetIncomeLoss, Cash, LongTermDebt, EarningsPerShareDiluted — and the tool resolves the right XBRL tag for that filer (post-ASC-606 companies use RevenueFromContractWithCustomerExcludingAssessedTax instead of "Revenues", etc.). Returns both ANNUAL (10-K) and QUARTERLY (10-Q) values by default, each labeled with fiscal_period (FY/Q1/Q2/Q3) and form, newest first, PLUS a `latest` field holding the single freshest data point. Use `latest` for point-in-time metrics like cash, runway, and debt — it is the newest 10-Q when one is more recent than the last 10-K, so a stale annual figure never masks a newer quarter. Use for "what was AAPL's revenue in 2024", "NVDA's latest cash position", "show me long-term debt trend", anything where you need the SEC-filed number rather than an estimate.

Input parameters:

- `cik` (string, required): Ticker (e.g., "AAPL") or CIK number (e.g., "320193"). Tickers are auto-resolved.
- `concept` (string, required): Metric name. Common: "Revenue" / "Revenues", "NetIncomeLoss", "Cash", "Assets", "Liabilities", "StockholdersEquity", "EarningsPerShareDiluted", "LongTermDebt".
- `period` (string): Which reporting periods to return: "all" (default — annual 10-K + quarterly 10-Q), "annual" (10-K/20-F/40-F only), or "quarterly" (10-Q only). Point-in-time metrics (cash/runway/debt) usually want th…

Output parameters:

- `annual_values` (array): Annual values sorted by fiscal year descending
- `cik` (string): Company CIK number
- `company_name` (string): Official company name
- `concept` (string): US-GAAP concept tag name
- `description` (string): Detailed concept description
- `label` (string): Human-readable concept label

### `edgar_insider_transactions` (~289 tokens)

Edgar Insider Transactions

AUTHORITATIVE insider trading activity (SEC Form 3/4/5) for a US public company — who bought or sold, how many shares, at what price, and what they hold now. Pass a ticker ("TSLA") or CIK. Returns each recent Form 4 filing parsed into structured transactions: reporting owner + role (director/officer/10% holder), transaction code (P=open-market purchase, S=sale, A=grant/award, M=option exercise, G=gift, F=tax-withholding), shares, price per share, acquired/disposed, and shares owned after. Use for "insider buying at $TICKER", "did executives sell recently", "latest Form 4 activity". Open-market purchases (code P) are the strongest conviction signal; awards (code A) are routine comp. For the raw filing list use edgar_company_filings with form_type:"4".

Input parameters:

- `include_derivatives` (boolean): Also include derivative (options/RSU) transactions. Default false (non-derivative common-stock only).
- `limit` (number): Max Form 4/3/5 filings to parse (1-25, default 10)
- `ticker_or_cik` (string, required): Ticker symbol (e.g., "TSLA") or CIK number (e.g., "1318605")

### `edgar_institutional_holdings` (~364 tokens)

Edgar Institutional Holdings

AUTHORITATIVE stock portfolio of a large institutional investor (SEC Form 13F-HR) — what a fund/manager owns, share counts, and position values. Pass the MANAGER's ticker or CIK (e.g. "BRK-B" or CIK "1067983" for Berkshire Hathaway; "1350694" for Bridgewater). Returns the latest quarterly 13F: top holdings aggregated by issuer with value (USD), shares, and % of portfolio, plus the report period. Use for "what does Berkshire own", "Bridgewater's biggest positions", "which funds hold $TICKER" (run per manager). Note: 13F covers US-listed long equity + options held by managers with >$100M AUM, filed ~45 days after quarter-end; it excludes shorts, cash, and non-US holdings. Values are whole USD for filings since 2023; older ones are in thousands. IMPORTANT: rows carry a `put_call` field and a plain-English `direction`. A `put` row is a BEARISH bet AGAINST that issuer — never report it as a holding the manager owns — and for option rows the value is the underlying's notional, not premium or capital at risk. Rank real holdings by `pct_of_long_equity`, and read `position_summary` + `interpretation_note` before summarising.

Input parameters:

- `limit` (number): Top N holdings by value to return (1-100, default 25)
- `ticker_or_cik` (string, required): The institutional manager's ticker (e.g. "BRK-B") or CIK (e.g. "1067983"). NOT the held stock — the fund/manager doing the filing.

### `edgar_fund_holdings` (~282 tokens)

Edgar Fund Holdings

AUTHORITATIVE portfolio holdings of a US ETF or mutual fund (SEC Form N-PORT) — what the fund actually owns. Pass the FUND's ticker (e.g. "ARKK", "QQQ", "VTI", "VOO", "IVV"). Returns the latest monthly portfolio: net assets, holdings count, and top positions by weight — each with name, CUSIP, value (USD), and % of fund. Use for "what does ARKK hold", "top holdings of QQQ", "is $STOCK in VTI". Distinct from edgar_institutional_holdings (13F = what an investment MANAGER like Berkshire owns); this is a registered fund's own N-PORT. Covers US-registered open-end funds + ETFs; data is ~30-60 days delayed. Note: a few legacy ETFs structured as unit investment trusts (e.g. SPY, DIA) don't file N-PORT and won't resolve — use IVV or VOO for S&P 500 exposure.

Input parameters:

- `limit` (number): Top N holdings by weight to return (1-100, default 25)
- `ticker` (string, required): ETF or mutual-fund ticker (e.g. "ARKK", "SPY", "QQQ"). Fund tickers, not company stock tickers.

### `edgar_ticker_to_cik` (~210 tokens)

Edgar Ticker To Cik

Resolve a US stock ticker (e.g. "TSLA") OR a company name (e.g. "Tesla", "Apple Inc") to the SEC's 10-digit CIK identifier — required by every other SEC tool. Call THIS FIRST when you have a ticker/name and need to use edgar_company_concept, edgar_company_filings, edgar_company_facts, sec_8k_recent, or any other SEC-keyed tool. Returns {cik, cik_padded, company_name, ticker, matched_by}; when matched by name it also returns `alternatives` for disambiguation. Cheap, no rate limit concerns. Most other tools also accept tickers/names directly and call this internally — only use it explicitly when you want the CIK as data.

Input parameters:

- `ticker` (string, required): Stock ticker symbol (e.g., "AAPL", "MSFT", "TSLA") or company name (e.g., "Apple", "Microsoft")

Output parameters:

- `cik` (string): Company CIK number
- `cik_padded` (string): CIK padded to 10 digits with leading zeros
- `company_name` (string): Official company name
- `ticker` (string): Stock ticker symbol

### `sponsor_to_filer` (~281 tokens)

Sponsor To Filer

Resolve an organization NAME — especially a clinical-trial sponsor, drug developer, or operating subsidiary — to the US-listed public FILER that reports it (ticker + SEC CIK). Built for the join that plain ticker/name lookup fails: trial registries (ClinicalTrials.gov) name operating subsidiaries ("Merck Sharp and Dohme"), while SEC names the listed parent ("Merck & Co", MRK). This tool bridges that gap and, crucially, tells you WHY a name does not resolve instead of collapsing every miss to "not found". Returns a `status`: "resolved" (name is itself a US-listed filer), "resolved_via_parent" (name is a subsidiary; resolved to its listed parent, with evidence + confidence), "us_registrant_unlisted" (has an SEC CIK but no public listing and no listed parent — typically a private company that filed a Form D or draft registration), or "no_us_registrant" (no US SEC presence at all — typically a non-US-listed or foreign private company). Use before joining trial sponsors to public financials, ownership, or filings.

Input parameters:

- `sponsor` (string, required): Organization name to resolve — a trial sponsor, drug developer, or company name, e.g. "Merck Sharp and Dohme", "Lexeo Therapeutics", "Dizal Pharmaceuticals".

### `edgar_xbrl_frames` (~356 tokens)

Edgar Xbrl Frames

Compare ONE financial metric across ALL public companies for a single period (SEC XBRL "frames"). PREFER OVER WEB SEARCH for "which companies had the most revenue/net income/assets in <year>", "rank companies by <metric>", cross-company financial comparison. concept is a US-GAAP tag (e.g. "Revenues", "NetIncomeLoss", "Assets", "ResearchAndDevelopmentExpense", "CashAndCashEquivalentsAtCarryingValue"). period is a calendar frame: "CY2023" (annual), "CY2023Q1" (quarter), or "CY2023Q1I" (instant/balance-sheet, period-end). Returns companies + values, sorted descending by default. Differs from edgar_company_concept (one company over time) — this is one period across every filer.

Input parameters:

- `concept` (string, required): US-GAAP (or dei) tag, e.g. "Revenues", "NetIncomeLoss", "Assets", "ResearchAndDevelopmentExpense".
- `limit` (number): Max companies to return (1-200, default 25).
- `period` (string, required): Calendar frame: "CY2023" (annual duration), "CY2023Q1" (quarterly duration), or "CY2023Q1I" (instant, balance-sheet items at period end).
- `sort` (string): "desc" (default, largest first) or "asc".
- `taxonomy` (string): Taxonomy: "us-gaap" (default) or "dei".
- `unit` (string): Unit of measure (default "USD"). Use "shares" for share counts, "USD-per-shares" for per-share.

### `edgar_filing_documents` (~540 tokens)

Edgar Filing Documents

AUTHORITATIVE list of the SEC filing documents inside ONE specific filing, by accession number. Retrieve a filing / its contents / attachments: pass the accession (e.g. "0000320193-25-000079", with or without dashes) plus the filer's ticker ("AAPL") or CIK ("320193"). Returns every document in the filing folder — the primary document (10-K / 10-Q / 8-K body), all exhibits, and XBRL files — each with name, type, size, and a direct https URL, plus the filing's form type, filing date, and human -index.html page. Set include_primary_text:true to also pull the primary document's text (HTML stripped to plaintext, ~40k chars). Use to list a 10-K / 10-Q / 8-K's exhibits, retrieve filing contents/attachments, or fetch the text of a filing. You can pass an exact accession, OR just a ticker + form_type to auto-resolve the latest matching filing (no accession lookup needed). Examples: edgar_filing_documents({ticker: "NVDA", form_type: "10-K"}) for the documents in NVIDIA's latest annual report; edgar_filing_documents({accession: "0000320193-25-000079", ticker: "AAPL", include_primary_text: true}) for a specific filing's text.

Input parameters:

- `accession` (string): Optional SEC accession number of a specific filing, with or without dashes (e.g. "0000320193-25-000079"). Omit it to auto-resolve the latest filing — pass form_type instead.
- `cik` (string): The filer's CIK number (e.g. "320193"). Provide this OR ticker.
- `form_type` (string): When accession is omitted, the form type of the latest filing to fetch, e.g. "10-K", "10-Q", "8-K", "DEF 14A". Omit both accession and form_type to get the single most recent filing of any type.
- `include_primary_text` (boolean): When true, also fetch the primary document and return its text (HTML stripped to plaintext, truncated to ~40,000 chars). Default false. For the FULL, pageable document text — or just one section like…
- `ticker` (string): The filer's ticker (e.g. "AAPL", "NVDA") or company name. Provide this OR cik. Tickers are auto-resolved to CIKs.

### `edgar_filing_text` (~526 tokens)

Edgar Filing Text

AUTHORITATIVE full text of a SEC filing's primary document (10-K / 10-Q / 8-K body), HTML stripped to clean plaintext — the source for disclosures that live in prose, not XBRL: going-concern language, ATM / at-the-market equity facilities, committed-equity share caps, public-float figures, subsequent events, and the liquidity footnote. Pass an accession (from edgar_search_filings / edgar_company_filings) plus the filer's ticker or CIK; OR omit accession and pass ticker + form_type to auto-resolve the latest matching filing. Optionally set `section` to return just one part (going_concern | liquidity | capital_resources | subsequent_events). Large docs (a 10-Q is ~100k+ chars of text) are PAGED, not spilled: the result caps at `max_chars` (default 50000) from `offset`, and returns `truncated` + `next_offset` — pass next_offset back as `offset` to read the next window. Use for "does $TICKER disclose substantial doubt / going concern", "what ATM facility does $TICKER have", "read the liquidity section of the latest 10-Q". For the list of documents/exhibits in a filing use edgar_filing_documents; for structured financial numbers use edgar_company_concept.

Input parameters:

- `accession` (string): SEC accession number, dashed or not (e.g. "0001683168-26-003909"). Omit to auto-resolve the latest filing of form_type for the given ticker/cik.
- `cik` (string): Filer CIK number (e.g. "1652935"). Provide this OR ticker.
- `form_type` (string): When accession is omitted, the form type of the latest filing to fetch — "10-K", "10-Q", "8-K", "DEF 14A", etc.
- `max_chars` (number): Max characters to return in this page (1000–100000, default 50000). Doc text past this is available via next_offset.
- `offset` (number): Character offset to start from (default 0). Pass the prior result's next_offset to page forward.
- `section` (string): Return only this section (located by heading). Omit for the whole document. Unmatched sections fall back to the whole document (section_found:false).
- `ticker` (string): Filer ticker (e.g. "ACTU"). Provide this OR cik.

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/pipeworx-io-edgar/edgar-mcp#diagnostics

## Score history

- 2026-08-03: 59
- 2026-08-02: 58
- 2026-08-01: 58
- 2026-07-31: 58
- 2026-07-30: 59
- 2026-07-29: 59
- 2026-07-28: 58
- 2026-07-27: 58
- 2026-07-26: 58

## Links

- Remote endpoint: https://gateway.pipeworx.io/edgar/mcp
- Repository: https://github.com/pipeworx-io/mcp-edgar
- Website: https://pipeworx.io/packs/edgar
- Changelog RSS feed: https://verifymcp.io/servers/pipeworx-io-edgar/edgar-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/pipeworx-io-edgar/edgar-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/pipeworx-io-edgar/edgar-mcp