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.comfor finance APIshttps://api.healthcare.comfor healthcare APIs
- You can optionally override the base URL using
API_REQUEST_BASE_URLenvironment 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:
{
"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
EndpointSearcher: Core class that handles:
- OpenAPI specification parsing
- Semantic search index creation
- Endpoint documentation formatting
- Natural language query processing
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
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - 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)
