fastapi_mcp

🥇Gold

FastAPI MCP allows you to seamlessly expose your FastAPI endpoints as Model Context Protocol tools, complete with authentication features. This integration enhances your AI applications by providing secure and efficient access to your models.

11,5878930Updated Today

Intermediate15 minutes to implementdevelopment

Saves ~160 min per use

Quick InstallView Source

claude install tadata-org/fastapi_mcp

Works with:

ClaudeCursorWindsurf

Overview

About This Skill

Use Cases

Integrate AI models with FastAPI for secure and efficient access to machine learning capabilities.

Build scalable AI applications with built-in authentication features to protect sensitive data.

Expose machine learning models as APIs for easy integration into existing applications and workflows.

Enhance the security of your AI applications by implementing robust authentication mechanisms.

Best For

RevOpsGrowth

Setup & Installation

Quick Install

Terminal

claude install tadata-org/fastapi_mcp

Alternative Install (Git Clone)

git clone https://github.com/tadata-org/fastapi_mcp

Requirements

Claude Code or compatible AI agent
Works with: Claude, Cursor, Windsurf

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I need to expose my FastAPI endpoints as Model Context Protocol tools for [COMPANY] in the [INDUSTRY]. Can you provide guidance on how to implement this with secure authentication features?

Example Output

### Implementation Steps for FastAPI MCP

1. **Install Dependencies**: Ensure you have FastAPI and the required libraries installed. Use `pip install fastapi uvicorn mcp`.
2. **Define Endpoints**: Create your FastAPI endpoints. For example:
   ```python
   from fastapi import FastAPI
   app = FastAPI()

   @app.get("/model")
   async def get_model_data():
       return {"data": "This is your model data."}
   ```
3. **Integrate MCP**: Wrap your endpoints with the MCP protocol. 
4. **Set Up Authentication**: Use OAuth2 or JWT for secure access. Ensure users are authenticated before accessing the endpoints.
5. **Test Your API**: Use tools like Postman or Curl to test your endpoints with authentication.

### Result
Your FastAPI application is now securely exposing its endpoints as Model Context Protocol tools, making it easier for AI applications to access the necessary data efficiently.