MCP Tooling bauen
Erstelle eigene MCP-Server mit FastMCP und integriere diese in meinGPT
Übersicht
Mit dem Model Context Protocol (MCP) kannst Du eigene Tools und Datenquellen für meinGPT erstellen. Diese Anleitung zeigt Dir, wie Du mit FastMCP schnell und effizient MCP-Server entwickelst und über den HTTP Streamable Transport in meinGPT einbindest.
Was ist MCP?
Das Model Context Protocol (MCP) ist ein standardisiertes Protokoll für die Kommunikation zwischen LLMs und externen Tools. MCP-Server können:
- Tools bereitstellen – Funktionen, die das LLM ausführen kann
- Resources anbieten – Datenquellen, die das LLM lesen kann
- Prompts definieren – Wiederverwendbare Vorlagen für Interaktionen
FastMCP Installation
pip install fastmcp uvicorn
Minimales Beispiel
Erstelle einen einfachen MCP-Server mit einem Tool:
from fastmcp import FastMCP
mcp = FastMCP("My MCP Server")
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
# Start with: uvicorn main:mcp --port 8000
FastAPI Integration
FastMCP lässt sich nahtlos in FastAPI-Anwendungen integrieren:
from fastmcp import FastMCP
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
# Create FastAPI app
app = FastAPI()
# Add CORS middleware for browser clients
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# Create MCP server
mcp = FastMCP("My MCP Server")
@mcp.tool()
async def multiply(a: float, b: float) -> float:
"""Multiply two numbers"""
return a * b
# Mount MCP as ASGI app
mcp_app = mcp.http_app(path='/mcp')
app.mount("/", mcp_app)
# Add health check
@app.get("/health")
async def health():
return {"status": "healthy"}
# Start: uvicorn main:app --reload
# MCP URL: http://localhost:8000/mcp
Authentication
FastMCP bietet flexible Authentifizierungsoptionen:
from fastmcp import FastMCP
from fastmcp.server.dependencies import get_http_request
from fastapi import HTTPException, status
mcp = FastMCP("Protected Server")
# API key management
API_KEYS = {"secret-key-1": "Production API Key"}
@mcp.tool()
async def protected_function(data: str) -> dict:
"""Protected function requiring authentication"""
request = get_http_request()
# Check API key from header
api_key = request.headers.get("X-API-Key")
if api_key not in API_KEYS:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid API key"
)
return {"status": "success", "data": data}
Resources
Resources bieten strukturierte Daten für das LLM:
@mcp.resource("config://settings")
async def get_settings():
"""Get current application settings"""
return {
"version": "1.0.0",
"environment": "production",
"features": ["api", "auth", "logging"]
}
@mcp.resource("data://{category}/{id}")
async def get_data(category: str, id: str):
"""Get data by category and ID"""
# Fetch from database
data = await fetch_from_db(category, id)
return data
Error Handling
Robuste Fehlerbehandlung ist essentiell:
import httpx
from typing import Optional, Dict, Any
@mcp.tool()
async def api_call(
endpoint: str,
method: str = "GET",
data: Optional[Dict[str, Any]] = None
) -> dict:
"""Make HTTP request with error handling"""
async with httpx.AsyncClient(timeout=30.0) as client:
try:
response = await client.request(
method=method,
url=endpoint,
json=data
)
response.raise_for_status()
return {
"success": True,
"data": response.json(),
"status_code": response.status_code
}
except httpx.HTTPStatusError as e:
return {
"success": False,
"error": f"HTTP {e.response.status_code}",
"status_code": e.response.status_code
}
except httpx.TimeoutException:
return {
"success": False,
"error": "Request timeout"
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
Context und Progress
Nutze Context für detailliertes Logging:
from fastmcp import Context
@mcp.tool()
async def process_data(file_path: str, ctx: Context) -> dict:
"""Process file with progress reporting"""
# Log information
await ctx.info(f"Processing file: {file_path}")
# Read file
data = await read_file(file_path)
# Report progress
await ctx.report_progress(50, f"Processing {len(data)} items")
# Process data
results = await process_items(data)
await ctx.report_progress(100, "Processing complete")
return {"processed": len(results), "results": results}
Best Practices
1. Klare Tool-Dokumentation
@mcp.tool()
async def send_email(to: str, subject: str, body: str) -> dict:
"""
Send an email notification.
Use this when:
- User explicitly requests to send an email
- System needs to send notifications
- Alerts need to be triggered
Args:
to: Recipient email address
subject: Email subject line
body: Email content (plain text)
Returns:
Dict with status and message_id
"""
# Implementation
2. Strukturierte Responses
from pydantic import BaseModel
from datetime import datetime
class APIResponse(BaseModel):
success: bool
data: Any = None
error: str = None
timestamp: datetime
@mcp.tool()
async def fetch_data(query: str) -> APIResponse:
"""Fetch data with structured response"""
try:
result = await database.query(query)
return APIResponse(
success=True,
data=result,
timestamp=datetime.now()
)
except Exception as e:
return APIResponse(
success=False,
error=str(e),
timestamp=datetime.now()
)
3. Dependency Injection
from fastmcp.server.dependencies import get_http_request
from typing import Annotated
from fastapi import Header
@mcp.tool()
async def database_query(
query: str,
db_url: Annotated[str, Header(alias="X-Database-URL")]
) -> list:
"""Execute database query with injected connection"""
# Use db_url from header
conn = await get_connection(db_url)
return await conn.fetch(query)
In meinGPT einbinden
- Starte Deinen MCP-Server
- Öffne die meinGPT-Einstellungen
- Navigiere zu "MCP Server"
- Füge einen neuen Server hinzu:
- Name: Dein Server-Name
- URL:
http://localhost:8000/mcp
- Transport: HTTP Streamable
- Optional: Header für Authentifizierung
Deployment
Für Produktion empfiehlt sich Containerisierung:
FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
Weiterführende Ressourcen
- FastMCP Dokumentation – Vollständige Referenz
- MCP Spezifikation – Protokoll Details
- FastMCP GitHub – Beispiele und Code
Beispielprojekte
Weitere Beispiele für MCP-Server finden Sie in der FastMCP Beispielsammlung.