MCP Server: Scalable OpenAPI Endpoint Discovery and API Request Tool

What is MCP Server? MCP Server is a scalable tool designed for OpenAPI endpoint discovery and API request handling, enabling efficient interaction with APIs through natural language queries. How to use MCP Server? To use MCP Server, deploy it via Docker with the appropriate OpenAPI JSON URL and API prefix, then interact with it through the MCP Client (Claude Desktop) by sending natural language requests. Key features of MCP Server? Utilizes remote OpenAPI JSON files without local file system access. Implements semantic search using an optimized MiniLM-L3 model for fast endpoint discovery. Supports async operations with FastAPI for improved performance. Handles large OpenAPI specifications by chunking endpoints for context preservation. Use cases of MCP Server? Discovering API endpoints based on user queries. Making API requests with complex parameters and headers. Integrating with various APIs in finance and healthcare sectors. FAQ from MCP Server? Can MCP Server handle large OpenAPI specifications? Yes, it can process specifications up to 10MB efficiently by indexing individual endpoints. Is there a cold start penalty? Yes, there is a cold start penalty of approximately 15 seconds for model loading if not using the Docker image. How can I install MCP Server? You can install it via Docker or using pip with the command: pip install mcp-server-any-openapi.

As an MCP (Model Context Protocol) server, MCP Server: Scalable OpenAPI Endpoint Discovery and API Request Tool enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

To use MCP Server, deploy it via Docker with the appropriate OpenAPI JSON URL and API prefix, then interact with it through the MCP Client (Claude Desktop) by sending natural language requests. Key features of MCP Server? Utilizes remote OpenAPI JSON files without local file system access. Implements semantic search using an optimized MiniLM-L3 model for fast endpoint discovery. Supports async operations with FastAPI for improved performance. Handles large OpenAPI specifications by chunking endpoints for context preservation. Use cases of MCP Server? Discovering API endpoints based on user queries. Making API requests with complex parameters and headers. Integrating with various APIs in finance and healthcare sectors. FAQ from MCP Server? Can MCP Server handle large OpenAPI specifications? Yes, it can process specifications up to 10MB efficiently by indexing individual endpoints. Is there a cold start penalty? Yes, there is a cold start penalty of approximately 15 seconds for model loading if not using the Docker image. How can I install MCP Server? You can install it via Docker or using pip with the command: pip install mcp-server-any-openapi.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.