Model intelligence for AI agents β syntax, params, pricing for 67+ generative AI models.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
Your agent thinks Midjourney still uses
--v 5. It doesn't β that flag was dropped at v7. It assumes DALL-E 3 and FLUX Schnell cost the same. They differ by ~50Γ. It confidently writes[Verse]tags for Suno. They were removed in v4.This MCP server fixes that. Real syntax, real prices, real recommendations for 67+ generative AI models β over the Model Context Protocol.
Works with: Claude Desktop Β· Claude Code Β· Cursor Β· Windsurf Β· Zed Β· Continue.dev Β· n8n Β· any stdio MCP client Domains: image Β· video Β· audio Β· text Β· code Cost to start: $0, no account, no API key
Your agent receives a brief β "30-second cinematic video of a thunderstorm at sea." β and instead of guessing, calls a tool.
The agent picks one, formats the prompt with optimize_prompt, lints the result with lint_prompt, checks get_pricing for the volume budget β all before a single token of generation cost is spent.
get_pricing({ model: "dall-e-3", volume: 100 }) returns actual USD cost plus cheaper alternatives β agents can finally optimize for budget, not just "vibes."recommend_model ranks across all five domains (image / video / audio / text / code) with reasoning, not guessing.lint_prompt catches deprecated flags, invalid parameters, and length violations before you burn credits.npx -y @promptibus/mcp β that's the install.Option A β Smithery (recommended):
Visit smithery.ai/server/@promptibus/mcp, pick your client, click install.
Option B β drop into your client's MCP config:
Option C β hosted HTTP endpoint (no install at all):
For clients that support HTTP transport:
Per-client paths are listed under Client configs below.
Every tool is available on every tier β including anonymous. Tiering applies to daily request limits and which models you can query against (free-tier covers 10 popular models; Pro/Studio unlocks all 67+).
| Tool | What it does | Example |
|---|---|---|
recommend_model | Top 3 models for a task, with reasoning + cost. | { task: "logo with embedded text", domain: "IMAGE" } |
optimize_prompt | Reformats a prompt for a specific model β applies model-specific syntax + community-tested wording. | { text: "a cat in space", model: "midjourney-v7" } |
lint_prompt | Finds deprecated flags, invalid parameters, length violations. Suggests fixes. | { prompt: "a cat --ar 16:9", model: "flux-2-pro" } |
compare_models | Side-by-side: provider, domain, cost, capabilities. 2β5 models. | { models: ["flux-2-pro","midjourney-v7"], criteria: "photorealism" } |
get_parameters | Recommended parameters: defaults, ranges, community configs. | { model: "stable-diffusion-3-5", task_type: "portrait" } |
get_model_profile | Full profile: capabilities, syntax guide, parameters, community tips, related prompts. | { model: "suno-v4" } |
get_pricing | Real USD pricing for a model / domain / planned volume. Includes cheaper alternatives. | { model: "dall-e-3", volume: 100 } |
"Which video model gives me the longest single shot under $10?"
β get_pricing({ domain: "VIDEO", volume: 60 }) returns a sorted matrix; agent picks the cheapest that meets duration.
"Convert this DALL-E prompt to Midjourney v7 syntax."
β optimize_prompt({ text: "...", model: "midjourney-v7" }) reformats β proper aspect-ratio flag, no --v, model-specific suffixes applied.
"Will this Suno prompt work with v4?"
β lint_prompt({ prompt: "[Verse] ...", model: "suno-v4" }) flags [Verse] as deprecated and proposes the v4 structure.
"I need to generate 1000 images at the cheapest viable quality."
β recommend_model filters by domain + budget; get_pricing validates total cost; agent ships under budget.
Browsable model profiles as MCP resources:
Each resource returns a Markdown profile (provider, domain, version, pricing, full guide). Useful for agents that want to surface model info as a sidebar.
The system-prompt MCP prompt exposes curated system prompts from the Promptibus community.
Anonymous users get full tool access β no account needed. Limits + model coverage scale with plan.
| Plan | Daily requests | Model coverage |
|---|---|---|
| Anonymous (no key) | 25 | 10 free-tier models |
| Free (with key) | 100 | 10 free-tier models |
| Pro | 500 | All 67+ models |
| Studio | 2,000 | All 67+ models |
Limits reset daily at midnight UTC. Plans + signup at promptibus.com/pricing.
Set PROMPTIBUS_API_KEY in your client config:
| Variable | Required | Purpose |
|---|---|---|
PROMPTIBUS_API_KEY | No | Higher rate limits, full model coverage. Get one at promptibus.com/settings/api-keys. |
PROMPTIBUS_API_URL | No | Override the API base (default https://promptibus.com). For self-hosted Promptibus or staging. |
Does this generate images, video, or audio? No. It tells your agent how to use whatever generation API the agent already has access to. Think of it as a prompt engineering co-pilot, not a router.
Do I need an account to start? No. Anonymous mode works out of the box (25 req/day, free-tier models). API key raises limits and unlocks all 67+ models.
Are my prompts logged?
Tool requests transit promptibus.com over HTTPS. We don't persist prompt bodies. API keys are SHA-256 hashed server-side; the raw key never lands in logs.
How fresh is the model data? Community-curated. New models typically appear within days of release; pricing is reviewed monthly. The data lives in a Postgres-backed catalogue at promptibus.com/models.
Does it work offline? The MCP server runs locally; the catalogue lives at promptibus.com. So: agent β MCP server is local stdio, MCP server β Promptibus is HTTPS. No internet, no answers.
Can I self-host the catalogue?
Yes. The Promptibus app is open-source β clone promptibus/promptibus, point PROMPTIBUS_API_URL at your deployment.
Is there an HTTP transport instead of stdio?
Yes β point your client at https://promptibus.com/api/mcp. Useful for sandboxed environments, browser-based MCP clients, and CI.
The client caches responses for tools whose output rarely changes (get_model_profile, get_parameters, compare_models, get_pricing). TTL: 24 h, in-memory per process. Cache is bypassed for tools whose output is input-dependent (recommend_model, optimize_prompt, lint_prompt).
promptibus.com; no third-party trackers in the request pathThe same npx -y @promptibus/mcp command works for every stdio client. Only the config file location and JSON shape differ.
~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
.cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
~/.codeium/windsurf/mcp_config.json:
settings.json:
~/.continue/config.json, under experimental.modelContextProtocolServers:
In the MCP Client node, set transport to stdio:
67+ models across 5 domains. Highlights:
Full catalogue: promptibus.com/models.
If @promptibus/mcp saves your agent from a wrong-syntax run or a $50 surprise on DALL-E volume, drop a star on the repo. Stars are how new MCP users discover quality servers in a sea of generic wrappers β it costs you a click and the next person ships faster.
MIT β Β© Promptibus
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