baryhuang/mcp-server-any-openapi

TODO
- The docker image is 2GB without pre-downloaded models. Its 3.76GB with pre-downloaded models!! Too big, someone please help me to reduce the size.
Configuration
Customize through environment variables. **GLOBAL_TOOL_PROMPT** is **IMPORTANT**!
- OPENAPI_JSON_DOCS_URL: URL to the OpenAPI specification JSON (defaults to https://api.staging.readymojo.com/openapi.json)
- MCP_API_PREFIX: Customizable tool namespace (default "any_openapi"):
- GLOBAL_TOOL_PROMPT: Optional text to prepend to all tool descriptions. This is crucial to make the Claude select and not select your tool accurately.
TL'DR
**Why I create this**: I want to serve my private API, whose swagger openapi docs is a few hundreds KB in size.
- Claude MCP simply error on processing these size of file
- I attempted convert the result to YAML, not small enough and a lot of errors. FAILED
- I attempted to provide a API category, then ask MCP Client (Claude Desktop) to get the api doc by group. Still too big, FAILED.
Eventually I came down to this solution:
- It uses in-memory semantic search to find relevant Api endpoints by natural language (such as list products)
- It returns the complete end-point docs (as I designed it to store one endpoint as one chunk) in millionseconds (as it's in memory)
**Boom**, Claude now knows what API to call, with the **full parameters**!
Wait I have to create another tool in this server to make the actual restful request, because "fetch" server simply don't work, and I don't want to debug why.
https://github.com/user-attachments/assets/484790d2-b5a7-475d-a64d-157e839ad9b0
Technical highlights:
Features
- 🧠 Use remote openapi json file as source, no local file system access, no updating required for API changes
- 🔍 Semantic search using optimized MiniLM-L3 model (43MB vs original 90MB)
- 🚀 FastAPI-based server with async support
- 🧠 Endpoint based chunking OpenAPI specs (handles 100KB+ documents), no loss of endpoint context
- ⚡ In-memory FAISS vector search for instant endpoint discovery
Limitations
- Not supporting linux/arm/v7 (build fails on Transformer library)
- 🐢 Cold start penalty (~15s for model loading) if not using docker image
- [Obsolete] Current docker image disabled downloading models. You have a dependency over huggingface. When you load the Claude Desktop, it takes some time to download the model. If huggingface is down, your server will not start.
- The latest docker image is embedding pre-downloaded models. If there is issues, I would revert to the old one.
Multi-instance config example
Here is the multi-instance config example. I design it so it can more flexibly used for multiple set of apis:
In this example:
- The server will automatically extract base URLs from the OpenAPI docs:
- https://api.finance.com for finance APIs
- https://api.healthcare.com for healthcare APIs
- You can optionally override the base URL using API_REQUEST_BASE_URL environment variable:
Claude Desktop Usage Example
Claude Desktop Project Prompt:
In chat, you can do:
Installation
Installing via Smithery
To install Scalable OpenAPI Endpoint Discovery and API Request Tool for Claude Desktop automatically via Smithery: