FastAPI MCP allows you to 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.
claude install tadata-org/fastapi_mcpFastAPI MCP allows you to 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.
["1. **Install the Required Packages**: Ensure you have FastAPI and the Model Context Protocol package installed. Use the command `pip install fastapi uvicorn model-context-protocol` to install these packages.","2. **Update Your FastAPI Application**: Modify your FastAPI application to include the MCP integration. Use the provided code snippets to expose your endpoints as tools.","3. **Configure Authentication**: Implement authentication for your endpoints. Use the HTTPBearer security scheme to ensure secure access to your tools.","4. **Run Your Application**: Start your FastAPI application using the command `uvicorn main:app --reload`. This will make your endpoints available as MCP tools."]
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.
claude install tadata-org/fastapi_mcpgit clone https://github.com/tadata-org/fastapi_mcpCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Integrate my FastAPI application located at [URL] with the Model Context Protocol (MCP) to expose endpoints as tools. Ensure the integration includes authentication using [AUTH_METHOD]. Provide the necessary code snippets and configuration steps to complete this setup.
To integrate your FastAPI application with the Model Context Protocol (MCP), follow these steps:
1. **Install the Required Packages**:
```bash
pip install fastapi uvicorn model-context-protocol
```
2. **Update Your FastAPI Application**:
```python
from fastapi import FastAPI
from model_context_protocol import MCP
app = FastAPI()
mcp = MCP(app)
@app.get("/tools/{tool_id}")
async def get_tool(tool_id: str):
return {"tool_id": tool_id, "status": "active"}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
```
3. **Configure Authentication**:
```python
from fastapi.security import HTTPBearer
security = HTTPBearer()
@app.get("/secure-tools/{tool_id}")
async def get_secure_tool(tool_id: str, security: HTTPBearer = Depends(security)):
return {"tool_id": tool_id, "status": "active", "authenticated": True}
```
4. **Run Your Application**:
```bash
uvicorn main:app --reload
```
Your FastAPI application is now integrated with MCP, and the endpoints are exposed as tools with authentication.Unlock data insights with interactive dashboards and collaborative analytics capabilities.
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