promptibus/mcp

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3 stars 2 forks Качество 76 Тренд 14MIT

Model intelligence MCP server — syntax, parameters, pricing for 67+ generative AI models (Midjourney, Flux, Suno, Runway, DALL-E, Stable Diffusion). 7 tools for any MCP client.

Обзор

Your agent thinks Midjourney still uses --v 5. It doesn't — that flag was dropped at v7. It assumes DALL-E 3 and FLUX Schnell cost the same. They differ by ~50×. It confidently writes [Verse] tags for Suno. They were removed in v4. Real syntax, real prices, real recommendations for 67+ generative AI models — over the Model Context Protocol. Claude Desktop · Claude Code · Cursor · Windsurf · Zed · Continue.dev · n8n · any stdio MCP client image · video · audio · text · code $0, no account, no API key Your agent receives a brief — "30-second cinematic video of a thunderstorm at sea." — 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 — all before a single token of generation cost is spent. - Your agent answers from a curated DB of 67+ models, not from training data that's 6 months stale.

README

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@promptibus/mcp — model intelligence for AI agents

Your agent thinks Midjourney still uses --v 5. It doesn’t — that flag was dropped at v7. It assumes DALL-E 3 and FLUX Schnell cost the same. They differ by ~50×. 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 — over the Model Context Protocol.

Works with: Claude Desktop · Claude Code · Cursor · Windsurf · Zed · Continue.dev · n8n · any stdio MCP client Domains: image · video · audio · text · code Cost to start: $0, no account, no API key

See it in action

Your agent receives a brief — “30-second cinematic video of a thunderstorm at sea.” — and instead of guessing, calls a tool.

recommend_model({ task: "30s cinematic video, thunderstorm at sea", domain: "VIDEO" })

Top 3 models for: "30s cinematic video, thunderstorm at sea"

1. Runway Gen-4 (Runway)
   Domain: VIDEO | Cost: 1 credit | Version: latest
   Improved temporal consistency, camera control, up to 20-second coherent clips.
   Source: https://promptibus.com/models/runway-gen-4

2. Sora (OpenAI)
   Domain: VIDEO | Cost: 1 credit | Version: latest
   Cinematic-quality clips from text prompts.
   Source: https://promptibus.com/models/sora

3. Veo 2 (Google)
   Domain: VIDEO | Cost: 1 credit | Version: latest
   High-fidelity clips with cinematic camera control.
   Source: https://promptibus.com/models/veo-2

The agent picks one, formats the prompt with optimize_prompt, lints the result with lint_prompt, checks get_pricing for the volume budget — all before a single token of generation cost is spent.

Why use this

Stops hallucination. Your agent answers from a curated DB of 67+ models, not from training data that’s 6 months stale.
Real money, real choices. get_pricing({ model: "dall-e-3", volume: 100 }) returns actual USD cost plus cheaper alternatives — agents can finally optimize for budget, not just “vibes.”
Right model for the job. recommend_model ranks across all five domains (image / video / audio / text / code) with reasoning, not guessing.

Открыть полный README на GitHub

Установка

npx -y @promptibus/mcp

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Конфигурация

{
  "mcpServers": {
    "promptibus": {
      "command": "npx",
      "args": ["-y", "@promptibus/mcp"]
    }
  }
}