What is Baryhuang Mcp Server Any Openapi

baryhuang_mcp-server-any-openapi is a scalable OpenAPI endpoint discovery and API request tool designed to facilitate the integration and usage of APIs through natural language queries. It leverages in-memory semantic search to quickly find relevant API endpoints and return complete documentation for those endpoints.

Use cases

Use cases include: 1) Integrating financial APIs to retrieve stock prices; 2) Accessing healthcare APIs for patient data management; 3) Enabling natural language queries for API endpoint discovery in applications; 4) Facilitating API requests in chatbots or virtual assistants.

How to use

To use baryhuang_mcp-server-any-openapi, you can install it via Smithery or pip. Configuration is done through environment variables, where you specify the OpenAPI JSON documentation URL and the MCP API prefix. After setup, you can interact with the server using natural language queries to discover API endpoints and make requests.

Key features

Key features include: 1) Remote OpenAPI JSON file usage without local file access; 2) Semantic search powered by an optimized MiniLM-L3 model; 3) FastAPI-based server with asynchronous support; 4) Endpoint-based chunking of OpenAPI specs for handling large documents; 5) In-memory FAISS vector search for instant endpoint discovery.

Where to use

baryhuang_mcp-server-any-openapi can be used in various fields such as finance, healthcare, and any domain requiring API integration and management. It is particularly useful for applications that need to handle large OpenAPI specifications efficiently.

MCP Server: Scalable OpenAPI Endpoint Discovery and API Request Tool

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.

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:

query -> [Embedding] -> FAISS TopK -> OpenAPI docs -> MCP Client (Claude Desktop)
MCP Client -> Construct OpenAPI Request -> Execute Request -> Return Response

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:

{
  "mcpServers": {
    "finance_openapi": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "OPENAPI_JSON_DOCS_URL=https://api.finance.com/openapi.json",
        "-e",
        "MCP_API_PREFIX=finance",
        "buryhuang/mcp-server-any-openapi:latest"
      ]
    },
    "healthcare_openapi": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "OPENAPI_JSON_DOCS_URL=https://api.healthcare.com/openapi.json",
        "-e",
        "MCP_API_PREFIX=healthcare",
        "buryhuang/mcp-server-any-openapi:latest"
      ]
    }
  }
}

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:

{
  "mcpServers": {
    "finance_openapi": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "OPENAPI_JSON_DOCS_URL=https://api.finance.com/openapi.json",
        "-e",
        "API_REQUEST_BASE_URL=https://api.finance.staging.com",
        "-e",
        "MCP_API_PREFIX=finance",
        "buryhuang/mcp-server-any-openapi:latest"
      ]
    }
  }
}

Claude Desktop Usage Example

Claude Desktop Project Prompt:

You should get the api spec details from tools financial_api_request_schema

You task is use financial_make_request tool to make the requests to get response. You should follow the api spec to add authorization header:
Authorization: Bearer <xxxxxxxxx>

Note: The base URL will be returned in the api_request_schema response, you don't need to specify it manually.

In chat, you can do:

Get prices for all stocks

Installation

Installing via Smithery

To install Scalable OpenAPI Endpoint Discovery and API Request Tool for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @baryhuang/mcp-server-any-openapi --client claude

Using pip

pip install mcp-server-any-openapi

Configuration

Customize through environment variables:

• 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”):

  # Creates tools: custom_api_request_schema and custom_make_request
  docker run -e MCP_API_PREFIX=finance ...
  

Available Tools

The server provides the following tools (where {prefix} is determined by MCP_API_PREFIX):

{prefix}_api_request_schema

Get API endpoint schemas that match your intent. Returns endpoint details including path, method, parameters, and response formats.

Input Schema:

{
  "query": {
    "type": "string",
    "description": "Describe what you want to do with the API (e.g., 'Get user profile information', 'Create a new job posting')"
  }
}

{prefix}_make_request

Essential for reliable execution with complex APIs where simplified implementations fail. Provides:

Input Schema:

{
  "method": {
    "type": "string",
    "description": "HTTP method (GET, POST, PUT, DELETE, PATCH)",
    "enum": [
      "GET",
      "POST",
      "PUT",
      "DELETE",
      "PATCH"
    ]
  },
  "url": {
    "type": "string",
    "description": "Fully qualified API URL (e.g., https://api.example.com/users/123)"
  },
  "headers": {
    "type": "object",
    "description": "Request headers (optional)",
    "additionalProperties": {
      "type": "string"
    }
  },
  "query_params": {
    "type": "object",
    "description": "Query parameters (optional)",
    "additionalProperties": {
      "type": "string"
    }
  },
  "body": {
    "type": "object",
    "description": "Request body for POST, PUT, PATCH (optional)"
  }
}

Response Format:

Docker Support

Multi-Architecture Builds

Official images support 3 platforms:

# Build and push using buildx
docker buildx create --use
docker buildx build --platform linux/amd64,linux/arm64 \
  -t buryhuang/mcp-server-any-openapi:latest \
  --push .

Flexible Tool Naming

Control tool names through MCP_API_PREFIX:

# Produces tools with "finance_api" prefix:
docker run -e MCP_API_PREFIX=finance_ ...

Supported Platforms

• linux/amd64
• linux/arm64

Option 1: Use Prebuilt Image (Docker Hub)

docker pull buryhuang/mcp-server-any-openapi:latest

Option 2: Local Development Build

docker build -t mcp-server-any-openapi .

Running the Container

docker run \
  -e OPENAPI_JSON_DOCS_URL=https://api.example.com/openapi.json \
  -e MCP_API_PREFIX=finance \
  buryhuang/mcp-server-any-openapi:latest

Key Components

1. EndpointSearcher: Core class that handles:

   • OpenAPI specification parsing
   • Semantic search index creation
   • Endpoint documentation formatting
   • Natural language query processing

2. Server Implementation:

   • Async FastAPI server
   • MCP protocol support
   • Tool registration and invocation handling

Running from Source

python -m mcp_server_any_openapi

Integration with Claude Desktop

Configure the MCP server in your Claude Desktop settings:

{
  "mcpServers": {
    "any_openapi": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "OPENAPI_JSON_DOCS_URL=https://api.example.com/openapi.json",
        "-e",
        "MCP_API_PREFIX=finance",
        "buryhuang/mcp-server-any-openapi:latest"
      ]
    }
  }
}

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the terms included in the LICENSE file.

Implementation Notes

• Endpoint-Centric Processing: Unlike document-level analysis that struggles with large specs, we index individual endpoints with:
  • Path + Method as unique identifiers
  • Parameter-aware embeddings
  • Response schema context
• Optimized Spec Handling: Processes OpenAPI specs up to 10MB (~5,000 endpoints) through:
  • Lazy loading of schema components
  • Parallel parsing of path items
  • Selective embedding generation (omits redundant descriptions)