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MCP Server: Scalable OpenAPI Endpoint Discovery and API Request Tool

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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 β€œfetch” 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
  • πŸ” Semantic search using optimized MiniLM-L3 model (43MB vs original 90MB)
  • πŸš€ 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 β€œany_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:

{
    "status_code": 200,
    "headers": {
        "content-type": "application/json",
        ...
    },
    "body": {
        // Response data
    }
}

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)
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