MCP Servers for FastAPI

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.

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  • Why this server?

    Powers the REST, GraphQL, and WebSocket API interfaces, enabling different methods of interacting with the AI models through standardized endpoints.

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    A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.
    5
    Python
    MIT License
  • Why this server?

    Automatically exposes FastAPI endpoints as Model Context Protocol (MCP) tools, preserving schemas and documentation

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    A zero-configuration tool that automatically exposes FastAPI endpoints as Model Context Protocol (MCP) tools, allowing LLM systems like Claude to interact with your API without additional coding.
    120
    Python
    MIT License
    • Linux
    • Apple
  • Why this server?

    The MCP server is implemented using the FastAPI framework, providing a web application with both MCP and standard web endpoints.

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    A Server-Sent Events implementation using FastAPI framework that integrates Model Context Protocol (MCP), allowing AI models to access external tools and data sources like weather information.
    6
    Python
    MIT License
  • Why this server?

    Mentioned in the example configuration where a FastAPI server is running, and the README notes it uses a FastMCP server with registered tools

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    An MCP server that exposes HTTP methods defined in an OpenAPI specification as tools, enabling interaction with APIs via the Model Context Protocol.
    2
    Python
    MIT License
  • Why this server?

    Uses FastAPI as the backend framework to serve the MCP server endpoints

  • Why this server?

    Integrates with a FastAPI hosted ML server to serve a trained Random Forest model for predictions and data processing.

  • Why this server?

    Uses FastAPI as the web framework for implementing the MCP server API endpoints

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    A server implementation that provides a unified interface for OpenAI services, Git repository analysis, and local filesystem operations through REST API endpoints.
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    • Linux
    • Apple
  • Why this server?

    Used for API implementation in the backend technical stack

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    A robust SSH server facilitating secure remote command execution with TMUX session management, multi-window support, and smart session recovery for improved AI-human interaction.
    3
    Python
  • Why this server?

    The MCP server is built on FastAPI, leveraging its ecosystem for automatic OpenAPI documentation generation, dependency injection, middleware support, validation, and async capabilities.

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    A production-ready MCP server built with FastAPI, providing an enhanced tool registry for creating, managing, and documenting AI tools for Large Language Models (LLMs).
    12
    Python
  • Why this server?

    The server uses FastAPI for its backend to handle requests and integrate with the PokeAPI database

  • Why this server?

    Used as the web framework for implementing the MCP server's API endpoints for bird detection data.

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    A Python-based server that enables accessing and analyzing bird detection data through the Model Context Protocol, offering features like filtering detections, accessing audio recordings, and generating reports.
    3
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    • Linux
    • Apple