Promptibus MCP
NPM · @PROMPTIBUS/MCP · SCANNED SEP 20
Model intelligence for AI agents — syntax, params, pricing for 67+ generative AI models.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security98
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 31 of 96 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency97
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to promptibus/mcp). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 131 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability81
- 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 2117 tokens (~176/item across 12 items; 12 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management93
- Stability observed for 28 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 12 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 12 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the Promptibus MCP server?
Promptibus MCP runs locally as an npm package, launched with npx -y @promptibus/mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · @promptibus/mcp
claude mcp add com-promptibus-mcp -- npx -y @promptibus/mcp
{
"mcpServers": {
"com-promptibus-mcp": {
"command": "npx",
"args": [
"-y",
"@promptibus/mcp"
]
}
}
} {
"servers": {
"com-promptibus-mcp": {
"command": "npx",
"args": [
"-y",
"@promptibus/mcp"
]
}
}
} codex mcp add com-promptibus-mcp -- npx -y @promptibus/mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"com-promptibus-mcp": {
"type": "local",
"command": [
"npx",
"-y",
"@promptibus/mcp"
],
"enabled": true
}
}
} openclaw mcp add com-promptibus-mcp --command npx --arg -y --arg @promptibus/mcp
mcp_servers:
com-promptibus-mcp:
command: "npx"
args: ["-y", "@promptibus/mcp"] {
"McpServers": {
"com-promptibus-mcp": {
"Transport": "stdio",
"Command": "npx",
"Arguments": [
"-y",
"@promptibus/mcp"
]
}
}
} assistant mcp add com-promptibus-mcp -t stdio -c npx -a -y @promptibus/mcp
{
"mcpServers": {
"com-promptibus-mcp": {
"command": "npx",
"args": [
"-y",
"@promptibus/mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 18 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 16 Sept 26 −2
- Stability: pass → 0.80 functional
- 15 Sept 26 0
- Stability: 0.97 → pass security
- 14 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
- 11 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 9 Sept 26 −2
- Stability: pass → 0.80 functional
- 8 Sept 26 0
- Stability: 0.97 → pass security
- 7 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed npm/@promptibus/mcp@0.9.0
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | npm |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | promptibus/mcp |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/promptibus/mcp/.github/workflows/release.yml@refs/tags/v0.9.0 |
| Rekor log index | 1503730092 |
| Predicate type | https://slsa.dev/provenance/v1 |
| Subject digest | sha512:042db6a4a01d288f43c28e2cd35eaf9f67ce4c66d0a53f91bd94230e202f596762a9baec04a7daf957b5d92227c01126668192749aa280da83fc967c9 |
Background: How many MCP packages publish verified provenance →
Dependencies 96 packages
| Packages resolved | 96 |
|---|---|
| Stale | 31 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
compare_models ~204
Show a SIDE-BY-SIDE diff (provider, cost, capabilities, license) of 2-5 models when the user is on the fence. Picks the cheapest one that actually fits. Use for 'should I use Flux or Midjourney for this?' / 'is Suno or Udio better for instrumental?' / 'GPT-4o vs Claude Sonnet for code'. Stops users from over-paying for the prestige model when a cheaper one delivers. Response includes `share_url` — give it to the user when they ask 'can I send this to my team?' or in any summary where the comparison would be useful to share.
| Name | Type | Req | Description |
|---|---|---|---|
| criteria | string | – | Decision-driving criteria — e.g. 'photorealism', 'speed at 1024px', 'price per image', 'commercial license clarity'. |
| models | array | yes | Model slugs being compared, e.g. ['flux-2-pro', 'midjourney-v7']. |
No output schema declared.
No examples provided.
count_tokens ~168
TOKEN COUNTER for any prompt + model. Saves the 'oh no the prompt was too long' API failure mode. Returns token count, encoding family used, chars/token ratio, and (when `model` is provided + has token-based pricing) the estimated cost in USD. Call BEFORE submitting long prompts — anything over 100k tokens needs to be checked. Free, no plan gate. Use this instead of importing tiktoken in the agent's own code.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | – | Optional model slug for accurate encoding + cost projection (e.g. gpt-4o, claude-sonnet-4-6, gpt-image-2). Defaults to GPT-4 (cl100k_base) approximation. |
| text | string | yes | The text to count tokens for. |
No output schema declared.
No examples provided.
format_prompt ~203
DETERMINISTIC syntax fixer — drops deprecated flags (Midjourney `--style raw` on v6+, Suno `[Verse]` on v4+, Midjourney-style flags on Flux/SD), applies model-correct aspect-ratio notation, normalizes whitespace. Lighter and faster than `optimize_prompt` (which uses community-tested wording + DB lookup). Use for quick syntax cleanup when you don't need a full rewrite — e.g. user gave you a Midjourney prompt but wants Flux output. Free, no plan gate.
| Name | Type | Req | Description |
|---|---|---|---|
| aspect | string | – | Optional aspect ratio (e.g. '16:9', '9:16'). Inserted as model-correct notation (Midjourney `--ar 16:9` vs Flux 'landscape, 16:9 aspect ratio'). |
| model | string | yes | Target model slug — drives which syntax rules apply. |
| text | string | yes | The prompt text to clean up. |
No output schema declared.
No examples provided.
get_model_profile ~122
Full intelligence dump on ONE model — what it's good at, what it's bad at, syntax quirks, hidden gotchas, community tips. Read this BEFORE recommending a model the user hasn't used. Saves you from suggesting Suno v4 when they wanted instrumental-only (it can't), Midjourney for transparent PNGs (no), Flux for inpainting (limited), etc. One call beats 10 trial generations.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | yes | Model slug to profile (e.g., midjourney-v7, flux-2-pro, suno-v5). |
No output schema declared.
No examples provided.
get_parameters ~143
Get the EXACT parameters that produce good output for this model + task type. Skips the 'tweak until it works' phase that wastes 20-50 paid generations. Community-tested configs (portrait / landscape / product-photo / cinematic-video / lo-fi-music / etc) — not the model's stale official defaults. Use whenever you're about to call model.generate(...) without explicit parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | yes | Model slug to fetch parameters for. |
| task_type | string | – | What the user is generating — e.g. 'portrait', 'landscape', 'product-photo', 'cinematic-video', 'instrumental-music'. Drives which preset is returned. |
No output schema declared.
No examples provided.
get_pricing ~253
ANSWER 'WHAT WILL THIS COST ME' before the user commits credit-card or burns API quota. Per-unit USD pricing + subscription plans + cheaper alternatives. Pass `{ model, volume: 100 }` and you get a total-cost estimate the user can act on. Use ANY TIME the conversation is heading toward 'let's generate N of X' — flag the bill BEFORE they pay it. Surfaces whether DALL-E HD ($0.08/img) or Midjourney standard ($30/mo unlimited-relaxed) is the cheaper path for their volume. When `volume >= 50` the response includes a `share_url` — useful when the user needs to send the cost estimate to a colleague or capture it for a doc.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | – | Filter: IMAGE, VIDEO, TEXT, CODE, or AUDIO. Ignored when `model` is provided. |
| model | string | – | Model slug (e.g., midjourney-v7, dall-e-3). Omit to query a whole domain or get an overview. |
| volume | integer | – | Planned generation count (images, seconds of video, etc). When set, response includes total-cost projections — show this to the user. |
No output schema declared.
No examples provided.
health_check ~86
FIRST CALL ON STARTUP — verifies Promptibus MCP is connected and returns your quota + tool catalog + recommended call order for generation tasks. Use this once at the start of any session where you have access to Promptibus — it calibrates which tools to reach for during reasoning. Cheap, free, no plan gate. Skip it and you'll re-derive the tool list from descriptions every time.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
lint_prompt ~154
Catch a BROKEN PROMPT before the user pays for a failed generation. Detects deprecated flags (e.g. Midjourney v5 `--style` on v6+), invalid parameters, length violations, incompatible combos, banned keywords. Returns issues + `savings` field with USD saved (per-unit cost of the avoided failed gen) when ERROR-severity issues are found. **Surface the savings to the user** — 'lint caught a broken prompt and saved you ~$0.04 of failed generation' is exactly the line owners want to read.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | yes | Target model slug to lint against. |
| prompt | string | yes | The prompt about to be sent to the model (post-optimization). |
No output schema declared.
No examples provided.
optimize_prompt ~179
Rewrite a raw user prompt into MODEL-SPECIFIC SYNTAX that actually works (Midjourney `--ar 16:9 --stylize 250`, Flux natural-language phrasing, SD weighted tokens, GPT-image style anchors). Cuts retry loops by 40-60%. Returns the optimized prompt + `savings` field with concrete USD saved (token diff for text/code models, ~1 avoided retry for image/video/audio). **Tell the user the savings number** in your task summary — owners see value, not vibes. Call BEFORE every paid generation.
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | yes | Target model slug (e.g., midjourney-v7, flux-2-pro, gpt-image-2). Use recommend_model first if unsure. |
| text | string | yes | The raw prompt, in plain English, as the user wrote it. |
No output schema declared.
No examples provided.
pick_cheapest_model ~210
ONE-SHOT 'CHEAPEST MODEL FOR THIS DOMAIN' answer. Faster than recommend_model when the user just wants the budget option. Returns top 3 by price + the pick + `savings` field with concrete USD saved per call vs the domain flagship (often 5-10x). **Tell the user the savings + project to their volume** — 'cheapest pick at $0.02/img saves ~$0.06/call vs Midjourney; for 100 images = $6 saved'. Use whenever cost is dominant constraint — startup MVP, throwaway prototype, content farm.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | yes | IMAGE | VIDEO | TEXT | CODE | AUDIO. |
| max_budget_usd | number | – | Hard ceiling per unit, e.g. 0.05 to filter out anything above $0.05 per image. |
| min_quality | string | – | Optional quality floor — currently advisory only; pricing-sorted result still includes a quality column the agent can self-filter. |
No output schema declared.
No examples provided.
recommend_model ~284
PICK THE CHEAPEST MODEL that fits BEFORE the user pays for the wrong one. Returns top 3 models ranked by fit + cost per call AND a `savings` field with USD saved per call vs the domain-flagship default. **Tell the user the savings number** in your task summary — that's the value they care about. Use when user says 'generate', 'make', 'create', 'draw', 'compose', 'write' anything but didn't pin a specific model. Avoids the 'I'll just default to GPT-4o / Midjourney v7 for everything' fail mode that overpays 5-10x. When savings exceed $0.50/call, response includes a `share_url` — surface it if the user might want to send the recommendation to a colleague.
| Name | Type | Req | Description |
|---|---|---|---|
| constraints | string | – | Budget/speed/quality bounds, e.g. 'under $0.05 per image', 'must run in <10s', 'commercial license required'. |
| domain | string | – | Optional filter when you know it: IMAGE, VIDEO, TEXT, CODE, or AUDIO. Skip and the tool auto-detects from the task wording. |
| task | string | yes | What the user wants generated, e.g. 'photorealistic product shot', 'instrumental jazz track', '30-second explainer video'. |
No output schema declared.
No examples provided.
whats_new ~111
RECENT MODEL ADDITIONS + UPDATES from the Promptibus catalog (default: last 7 days). Use whenever you need to know if a newer/better model is available for the task — replaces 'I'll just use the one I know about' fail mode. Surfaces new models the user might want to try before committing to defaults. Free, no plan gate.
| Name | Type | Req | Description |
|---|---|---|---|
| days | integer | – | Lookback window (default 7). Set to 30 for a wider scope when the catalog has been quiet. |
No output schema declared.
No examples provided.
What is the Promptibus MCP server?
Promptibus MCP is listed in the public MCP registry as com.promptibus/mcp. Model intelligence for AI agents, syntax, params, pricing for 67+ generative AI models. This page covers its npm package (@promptibus/mcp).
Is the Promptibus MCP server safe to use?
Promptibus MCP scores 94 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. It declares no install or post-install scripts. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Promptibus MCP server expose?
Promptibus MCP exposes 12 tools: recommend_model, optimize_prompt, lint_prompt, compare_models, get_parameters, and 7 more. Their descriptions and schemas cost roughly 2,117 tokens of context every time the server is loaded.
Is the Promptibus MCP server still maintained?
Promptibus MCP is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the Promptibus MCP server under?
Promptibus MCP declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.