wehub-resource-sync
37 KiB
37 KiB
MCP-Server-Implementierung
🎯 Was dieses Lab abdeckt
Dieses praktische Lab führt Sie durch die Implementierung eines produktionsreifen MCP-Servers mit dem FastMCP-Framework. Sie werden die Kernstruktur des Servers aufbauen, die Datenbankintegration implementieren, Tools für den Datenzugriff erstellen und die Grundlage für KI-gestützte Einzelhandelsanalysen schaffen.
Überblick
Der MCP-Server ist das Herzstück unserer Lösung für Einzelhandelsanalysen. Er fungiert als Brücke zwischen KI-Assistenten und der PostgreSQL-Datenbank und bietet sicheren, intelligenten Zugriff auf Geschäftsdaten über ein standardisiertes Protokoll.
Dieses Lab zeigt Ihnen, wie Sie einen robusten, skalierbaren MCP-Server gemäß Unternehmensmustern und Best Practices entwickeln.
Lernziele
Am Ende dieses Labs werden Sie in der Lage sein:
- Einen MCP-Server mit FastMCP und einer geeigneten Architektur und Organisation zu erstellen
- Datenbankintegration mit Connection-Pooling und Fehlerbehandlung zu implementieren
- MCP-Tools für Datenbankschema-Analyse und Abfrageausführung zu entwickeln
- Row Level Security (RLS) Kontextmanagement zu konfigurieren
- Überwachungs- und Monitoring-Funktionen hinzuzufügen
- Ihre MCP-Server-Implementierung lokal und mit VS Code zu testen
📁 Projektstruktur
Werfen wir einen Blick auf die Organisation des MCP-Servers:
mcp_server/
├── __init__.py # Package initialization
├── config.py # Configuration management
├── health_check.py # Health monitoring endpoints
├── sales_analysis.py # Main MCP server implementation
├── sales_analysis_postgres.py # Database integration layer
└── sales_analysis_text_embeddings.py # AI/semantic search integration
🔧 Konfigurationsmanagement
Umgebungskonfiguration (config.py)
Zuerst erstellen wir ein robustes Konfigurationssystem:
# mcp_server/config.py
"""
Configuration management for the MCP server.
Handles environment variables, validation, and defaults.
"""
import os
import logging
from typing import Optional, Dict, Any
from dataclasses import dataclass
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
logger = logging.getLogger(__name__)
@dataclass
class DatabaseConfig:
"""Database connection configuration."""
host: str
port: int
database: str
user: str
password: str
min_connections: int = 2
max_connections: int = 10
command_timeout: int = 30
@classmethod
def from_env(cls) -> 'DatabaseConfig':
"""Create configuration from environment variables."""
return cls(
host=os.getenv('POSTGRES_HOST', 'localhost'),
port=int(os.getenv('POSTGRES_PORT', '5432')),
database=os.getenv('POSTGRES_DB', 'zava'),
user=os.getenv('POSTGRES_USER', 'postgres'),
password=os.getenv('POSTGRES_PASSWORD', ''),
min_connections=int(os.getenv('POSTGRES_MIN_CONNECTIONS', '2')),
max_connections=int(os.getenv('POSTGRES_MAX_CONNECTIONS', '10')),
command_timeout=int(os.getenv('POSTGRES_COMMAND_TIMEOUT', '30'))
)
def to_asyncpg_params(self) -> Dict[str, Any]:
"""Convert to asyncpg connection parameters."""
return {
'host': self.host,
'port': self.port,
'database': self.database,
'user': self.user,
'password': self.password,
'command_timeout': self.command_timeout,
'server_settings': {
'application_name': 'zava-mcp-server',
'jit': 'off', # Disable JIT for stability
'work_mem': '4MB',
'statement_timeout': f'{self.command_timeout}s'
}
}
@dataclass
class AzureConfig:
"""Azure AI services configuration."""
project_endpoint: str
openai_endpoint: str
embedding_model_deployment: str
client_id: str
client_secret: str
tenant_id: str
@classmethod
def from_env(cls) -> 'AzureConfig':
"""Create configuration from environment variables."""
return cls(
project_endpoint=os.getenv('PROJECT_ENDPOINT', ''),
openai_endpoint=os.getenv('AZURE_OPENAI_ENDPOINT', ''),
embedding_model_deployment=os.getenv('EMBEDDING_MODEL_DEPLOYMENT_NAME', 'text-embedding-3-small'),
client_id=os.getenv('AZURE_CLIENT_ID', ''),
client_secret=os.getenv('AZURE_CLIENT_SECRET', ''),
tenant_id=os.getenv('AZURE_TENANT_ID', '')
)
def is_configured(self) -> bool:
"""Check if all required Azure configuration is present."""
return all([
self.project_endpoint,
self.openai_endpoint,
self.client_id,
self.client_secret,
self.tenant_id
])
@dataclass
class ServerConfig:
"""MCP server configuration."""
host: str = '0.0.0.0'
port: int = 8000
log_level: str = 'INFO'
enable_cors: bool = True
enable_health_check: bool = True
applicationinsights_connection_string: Optional[str] = None
@classmethod
def from_env(cls) -> 'ServerConfig':
"""Create configuration from environment variables."""
return cls(
host=os.getenv('MCP_SERVER_HOST', '0.0.0.0'),
port=int(os.getenv('MCP_SERVER_PORT', '8000')),
log_level=os.getenv('LOG_LEVEL', 'INFO').upper(),
enable_cors=os.getenv('ENABLE_CORS', 'true').lower() == 'true',
enable_health_check=os.getenv('ENABLE_HEALTH_CHECK', 'true').lower() == 'true',
applicationinsights_connection_string=os.getenv('APPLICATIONINSIGHTS_CONNECTION_STRING')
)
class MCPServerConfig:
"""Main configuration class for the MCP server."""
def __init__(self):
self.database = DatabaseConfig.from_env()
self.azure = AzureConfig.from_env()
self.server = ServerConfig.from_env()
# Validate configuration
self._validate_config()
def _validate_config(self):
"""Validate configuration and log warnings for missing values."""
if not self.database.password:
logger.warning("Database password is empty. This may cause connection issues.")
if not self.azure.is_configured():
logger.warning("Azure configuration is incomplete. AI features may not work.")
logger.info(f"Configuration loaded - Database: {self.database.host}:{self.database.port}")
logger.info(f"Server will run on {self.server.host}:{self.server.port}")
# Global configuration instance
config = MCPServerConfig()
Wichtige Konfigurationsmerkmale
- Laden von Umgebungsvariablen: Automatische Unterstützung für .env-Dateien
- Typensicherheit: Validierung mit Dataclasses und Typ-Hinweisen
- Flexible Standardwerte: Sinnvolle Standardwerte für die Entwicklung
- Validierung: Konfigurationsvalidierung mit hilfreichen Fehlermeldungen
- Sicherheit: Sensible Werte nur aus Umgebungsvariablen
🗄️ Datenbank-Integrationsschicht
PostgreSQL-Provider (sales_analysis_postgres.py)
Implementieren wir die Datenbank-Integrationsschicht:
# mcp_server/sales_analysis_postgres.py
"""
PostgreSQL database integration for MCP server.
Handles connections, queries, and schema introspection.
"""
import asyncio
import asyncpg
import logging
from typing import Dict, Any, List, Optional, Tuple
from contextlib import asynccontextmanager
from datetime import datetime
import json
from .config import config
logger = logging.getLogger(__name__)
class PostgreSQLSchemaProvider:
"""Provides PostgreSQL database access and schema information."""
def __init__(self):
self.connection_pool: Optional[asyncpg.Pool] = None
self.postgres_config = config.database.to_asyncpg_params()
async def create_pool(self) -> None:
"""Create connection pool for database operations."""
if self.connection_pool is None:
try:
self.connection_pool = await asyncpg.create_pool(
**self.postgres_config,
min_size=config.database.min_connections,
max_size=config.database.max_connections,
max_inactive_connection_lifetime=300 # 5 minutes
)
logger.info("Database connection pool created successfully")
except Exception as e:
logger.error(f"Failed to create database connection pool: {e}")
raise
async def close_pool(self) -> None:
"""Close the connection pool."""
if self.connection_pool:
await self.connection_pool.close()
self.connection_pool = None
logger.info("Database connection pool closed")
@asynccontextmanager
async def get_connection(self):
"""Get a database connection from the pool."""
if not self.connection_pool:
await self.create_pool()
async with self.connection_pool.acquire() as connection:
yield connection
async def set_rls_context(self, connection: asyncpg.Connection, rls_user_id: str) -> None:
"""Set Row Level Security context for the connection."""
try:
await connection.execute(
"SELECT set_config('app.current_rls_user_id', $1, false)",
rls_user_id
)
logger.debug(f"RLS context set for user: {rls_user_id}")
except Exception as e:
logger.error(f"Failed to set RLS context: {e}")
raise
async def get_table_schema(self, table_name: str, rls_user_id: str) -> Dict[str, Any]:
"""Get detailed schema information for a specific table."""
async with self.get_connection() as conn:
await self.set_rls_context(conn, rls_user_id)
# Parse schema and table name
if '.' in table_name:
schema_name, table_name = table_name.split('.', 1)
else:
schema_name = 'retail' # Default schema
# Get column information
columns_query = """
SELECT
column_name,
data_type,
is_nullable,
column_default,
character_maximum_length,
numeric_precision,
numeric_scale,
ordinal_position
FROM information_schema.columns
WHERE table_schema = $1 AND table_name = $2
ORDER BY ordinal_position
"""
columns = await conn.fetch(columns_query, schema_name, table_name)
if not columns:
raise ValueError(f"Table {schema_name}.{table_name} not found or not accessible")
# Get foreign key relationships
fk_query = """
SELECT
kcu.column_name,
ccu.table_schema AS foreign_table_schema,
ccu.table_name AS foreign_table_name,
ccu.column_name AS foreign_column_name
FROM information_schema.table_constraints tc
JOIN information_schema.key_column_usage kcu
ON tc.constraint_name = kcu.constraint_name
JOIN information_schema.constraint_column_usage ccu
ON ccu.constraint_name = tc.constraint_name
WHERE tc.constraint_type = 'FOREIGN KEY'
AND tc.table_schema = $1
AND tc.table_name = $2
"""
foreign_keys = await conn.fetch(fk_query, schema_name, table_name)
# Get indexes
index_query = """
SELECT
indexname,
indexdef
FROM pg_indexes
WHERE schemaname = $1 AND tablename = $2
"""
indexes = await conn.fetch(index_query, schema_name, table_name)
# Format schema information
schema_info = {
"table_name": f"{schema_name}.{table_name}",
"columns": [
{
"name": col["column_name"],
"type": col["data_type"],
"nullable": col["is_nullable"] == "YES",
"default": col["column_default"],
"max_length": col["character_maximum_length"],
"precision": col["numeric_precision"],
"scale": col["numeric_scale"],
"position": col["ordinal_position"]
}
for col in columns
],
"foreign_keys": [
{
"column": fk["column_name"],
"references": f"{fk['foreign_table_schema']}.{fk['foreign_table_name']}.{fk['foreign_column_name']}"
}
for fk in foreign_keys
],
"indexes": [
{
"name": idx["indexname"],
"definition": idx["indexdef"]
}
for idx in indexes
]
}
return schema_info
async def get_multiple_table_schemas(
self,
table_names: List[str],
rls_user_id: str
) -> str:
"""Get schema information for multiple tables."""
schemas = []
for table_name in table_names:
try:
schema = await self.get_table_schema(table_name, rls_user_id)
schemas.append(self._format_schema_for_ai(schema))
except Exception as e:
logger.warning(f"Failed to get schema for {table_name}: {e}")
schemas.append(f"Error retrieving schema for {table_name}: {str(e)}")
return "\n\n".join(schemas)
def _format_schema_for_ai(self, schema: Dict[str, Any]) -> str:
"""Format schema information for AI consumption."""
table_name = schema["table_name"]
columns = schema["columns"]
foreign_keys = schema["foreign_keys"]
# Create column definitions
column_lines = []
for col in columns:
nullable = "NULL" if col["nullable"] else "NOT NULL"
type_info = col["type"]
if col["max_length"]:
type_info += f"({col['max_length']})"
elif col["precision"] and col["scale"]:
type_info += f"({col['precision']},{col['scale']})"
default_info = f" DEFAULT {col['default']}" if col["default"] else ""
column_lines.append(f" {col['name']} {type_info} {nullable}{default_info}")
# Create foreign key information
fk_lines = []
for fk in foreign_keys:
fk_lines.append(f" {fk['column']} -> {fk['references']}")
# Combine into readable format
schema_text = f"Table: {table_name}\n"
schema_text += "Columns:\n" + "\n".join(column_lines)
if fk_lines:
schema_text += "\n\nForeign Keys:\n" + "\n".join(fk_lines)
return schema_text
async def execute_query(
self,
sql_query: str,
rls_user_id: str,
max_rows: int = 20
) -> str:
"""Execute a SQL query with Row Level Security context."""
async with self.get_connection() as conn:
await self.set_rls_context(conn, rls_user_id)
try:
# Set a query timeout
rows = await asyncio.wait_for(
conn.fetch(sql_query),
timeout=config.database.command_timeout
)
if not rows:
return "Query executed successfully. No rows returned."
# Limit result set size
limited_rows = rows[:max_rows]
# Format results
result = self._format_query_results(limited_rows, len(rows), max_rows)
logger.info(f"Query executed successfully. Returned {len(limited_rows)} rows.")
return result
except asyncio.TimeoutError:
error_msg = f"Query timeout after {config.database.command_timeout} seconds"
logger.error(error_msg)
raise Exception(error_msg)
except Exception as e:
logger.error(f"Query execution failed: {e}")
raise
def _format_query_results(
self,
rows: List[asyncpg.Record],
total_rows: int,
max_rows: int
) -> str:
"""Format query results for AI consumption."""
if not rows:
return "No results found."
# Get column names
columns = list(rows[0].keys())
# Create header
result_lines = [f"Results ({len(rows)} of {total_rows} rows):"]
result_lines.append("=" * 50)
# Add column headers
header = " | ".join(columns)
result_lines.append(header)
result_lines.append("-" * len(header))
# Add data rows
for row in rows:
formatted_values = []
for col in columns:
value = row[col]
if value is None:
formatted_values.append("NULL")
elif isinstance(value, datetime):
formatted_values.append(value.strftime("%Y-%m-%d %H:%M:%S"))
elif isinstance(value, (dict, list)):
formatted_values.append(json.dumps(value))
else:
formatted_values.append(str(value))
result_lines.append(" | ".join(formatted_values))
# Add truncation notice if needed
if total_rows > max_rows:
result_lines.append(f"\n... and {total_rows - max_rows} more rows (truncated for display)")
return "\n".join(result_lines)
async def get_current_utc_date(self) -> str:
"""Get current UTC date/time."""
async with self.get_connection() as conn:
result = await conn.fetchval("SELECT NOW() AT TIME ZONE 'UTC'")
return result.isoformat() + "Z"
async def health_check(self) -> Dict[str, Any]:
"""Perform database health check."""
try:
async with self.get_connection() as conn:
# Simple connectivity test
result = await conn.fetchval("SELECT 1")
# Check pool status
pool_info = {
"min_size": self.connection_pool._minsize if self.connection_pool else 0,
"max_size": self.connection_pool._maxsize if self.connection_pool else 0,
"current_size": self.connection_pool.get_size() if self.connection_pool else 0,
"idle_size": self.connection_pool.get_idle_size() if self.connection_pool else 0
}
return {
"status": "healthy",
"database_responsive": result == 1,
"pool_info": pool_info
}
except Exception as e:
return {
"status": "unhealthy",
"error": str(e)
}
# Global database provider instance
db_provider = PostgreSQLSchemaProvider()
Wichtige Merkmale der Datenbankschicht
- Connection-Pooling: Effizientes Ressourcenmanagement mit asyncpg
- RLS-Integration: Automatische Einstellung des Row Level Security-Kontexts
- Schema-Analyse: Dynamische Entdeckung von Tabellenschemata
- Fehlerbehandlung: Umfassendes Fehlermanagement und Logging
- Abfrageformatierung: KI-freundliche Ergebnisformatierung
- Überwachung: Überprüfung der Datenbankverbindung und des Poolstatus
🔧 Hauptimplementierung des MCP-Servers
FastMCP-Server (sales_analysis.py)
Nun implementieren wir den Haupt-MCP-Server:
# mcp_server/sales_analysis.py
"""
Main MCP server implementation for Zava Retail Sales Analysis.
Provides AI assistants with secure access to retail database.
"""
import logging
import asyncio
from typing import Dict, Any, List, Annotated
from contextlib import asynccontextmanager
from fastmcp import FastMCP, Context
from pydantic import Field
from .config import config
from .sales_analysis_postgres import db_provider
from .health_check import setup_health_endpoints
# Configure logging
logging.basicConfig(
level=getattr(logging, config.server.log_level),
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# Create FastMCP server instance
mcp = FastMCP("Zava Retail Sales Analysis")
# List of valid tables for schema access
VALID_TABLES = [
"retail.stores",
"retail.customers",
"retail.categories",
"retail.product_types",
"retail.products",
"retail.orders",
"retail.order_items",
"retail.inventory"
]
def get_rls_user_id(ctx: Context) -> str:
"""Extract Row Level Security User ID from request context."""
# In HTTP mode, get from headers
if hasattr(ctx, 'headers') and ctx.headers:
rls_user_id = ctx.headers.get("x-rls-user-id")
if rls_user_id:
logger.debug(f"RLS User ID from headers: {rls_user_id}")
return rls_user_id
# Default fallback for development/testing
default_id = "00000000-0000-0000-0000-000000000000"
logger.warning(f"No RLS User ID found, using default: {default_id}")
return default_id
@mcp.tool()
async def get_multiple_table_schemas(
ctx: Context,
table_names: Annotated[List[str], Field(description="List of table names to retrieve schemas for. Valid tables: " + ", ".join(VALID_TABLES))]
) -> str:
"""
Retrieve database schemas for multiple tables in a single request.
This tool provides comprehensive schema information including:
- Column names, types, and constraints
- Foreign key relationships
- Index information
- Table structure for AI query planning
Args:
table_names: List of valid table names from the retail schema
Returns:
Formatted schema information for all requested tables
"""
rls_user_id = get_rls_user_id(ctx)
# Validate table names
invalid_tables = [table for table in table_names if table not in VALID_TABLES]
if invalid_tables:
logger.warning(f"Invalid table names requested: {invalid_tables}")
return f"Error: Invalid table names: {', '.join(invalid_tables)}. Valid tables are: {', '.join(VALID_TABLES)}"
try:
logger.info(f"Retrieving schemas for tables: {table_names} (User: {rls_user_id})")
result = await db_provider.get_multiple_table_schemas(table_names, rls_user_id)
return result
except Exception as e:
logger.error(f"Error retrieving table schemas: {e}")
return f"Error retrieving table schemas: {e!s}"
@mcp.tool()
async def execute_sales_query(
ctx: Context,
postgresql_query: Annotated[str, Field(description="A well-formed PostgreSQL query to execute against the retail database. Always get table schemas first before writing queries.")]
) -> str:
"""
Execute PostgreSQL queries against the retail sales database with Row Level Security.
This tool allows AI assistants to run analytical queries on retail data including:
- Sales performance analysis
- Customer behavior insights
- Inventory management queries
- Product performance metrics
- Store-specific reporting
Important: Row Level Security ensures users only see data they're authorized to access.
Args:
postgresql_query: SQL query to execute (automatically filtered by RLS)
Returns:
Query results formatted for AI analysis (limited to 20 rows for readability)
"""
rls_user_id = get_rls_user_id(ctx)
try:
logger.info(f"Executing query for user: {rls_user_id}")
logger.debug(f"Query: {postgresql_query[:100]}...")
result = await db_provider.execute_query(postgresql_query, rls_user_id)
return result
except Exception as e:
logger.error(f"Error executing database query: {e}")
return f"Error executing database query: {e!s}"
@mcp.tool()
async def get_current_utc_date(ctx: Context) -> str:
"""
Get the current UTC date and time in ISO format.
Useful for time-sensitive queries and date-based analysis.
Returns:
Current UTC date/time in ISO format (YYYY-MM-DDTHH:MM:SS.fffffZ)
"""
try:
result = await db_provider.get_current_utc_date()
logger.debug(f"Current UTC date retrieved: {result}")
return result
except Exception as e:
logger.error(f"Error getting current UTC date: {e}")
return f"Error getting current UTC date: {e!s}"
# Application lifecycle management
@asynccontextmanager
async def lifespan(app):
"""Manage application startup and shutdown."""
logger.info("Starting Zava Retail MCP Server...")
try:
# Initialize database connection pool
await db_provider.create_pool()
logger.info("Database connection pool initialized")
# Test database connectivity
health_status = await db_provider.health_check()
if health_status["status"] != "healthy":
logger.error(f"Database health check failed: {health_status}")
raise Exception("Database not healthy")
logger.info("MCP Server startup complete")
yield
except Exception as e:
logger.error(f"Startup failed: {e}")
raise
finally:
# Cleanup
logger.info("Shutting down MCP Server...")
await db_provider.close_pool()
logger.info("MCP Server shutdown complete")
# Configure server application
def create_app():
"""Create and configure the MCP server application."""
# Get the FastMCP app instance
app = mcp.sse_app()
# Set up lifecycle management
app.router.lifespan_context = lifespan
# Add health check endpoints if enabled
if config.server.enable_health_check:
setup_health_endpoints(app, db_provider)
# Configure CORS if enabled
if config.server.enable_cors:
from fastapi.middleware.cors import CORSMiddleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Configure appropriately for production
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
logger.info(f"MCP Server configured - CORS: {config.server.enable_cors}, Health: {config.server.enable_health_check}")
return app
# Create the application instance
app = create_app()
# Main entry point for development
if __name__ == "__main__":
import uvicorn
logger.info(f"Starting development server on {config.server.host}:{config.server.port}")
uvicorn.run(
"sales_analysis:app",
host=config.server.host,
port=config.server.port,
reload=True,
log_level=config.server.log_level.lower()
)
Wichtige Merkmale des MCP-Servers
- Tool-Registrierung: Deklarative Tool-Definitionen mit Typensicherheit
- RLS-Kontextmanagement: Automatische Extraktion der Benutzeridentität und Kontexteinstellung
- Fehlerbehandlung: Umfassendes Fehlermanagement mit benutzerfreundlichen Nachrichten
- Lebenszyklusmanagement: Korrektes Starten/Herunterfahren mit Ressourcenbereinigung
- Überwachung: Eingebaute Endpunkte für Gesundheitschecks
- Entwicklungsunterstützung: Hot-Reload und Debugging-Funktionen
🏥 Überwachung der Servergesundheit
Implementierung von Gesundheitschecks (health_check.py)
# mcp_server/health_check.py
"""
Health check endpoints for monitoring MCP server status.
"""
import logging
from typing import Dict, Any
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
logger = logging.getLogger(__name__)
def setup_health_endpoints(app: FastAPI, db_provider) -> None:
"""Add health check endpoints to the FastAPI application."""
@app.get("/health")
async def health_check() -> JSONResponse:
"""Basic health check endpoint."""
return JSONResponse(
status_code=200,
content={
"status": "healthy",
"service": "zava-retail-mcp-server",
"timestamp": await db_provider.get_current_utc_date()
}
)
@app.get("/health/detailed")
async def detailed_health_check() -> JSONResponse:
"""Detailed health check including database connectivity."""
health_status = {
"service": "zava-retail-mcp-server",
"status": "healthy",
"components": {}
}
overall_healthy = True
# Check database
try:
db_health = await db_provider.health_check()
health_status["components"]["database"] = db_health
if db_health["status"] != "healthy":
overall_healthy = False
except Exception as e:
health_status["components"]["database"] = {
"status": "unhealthy",
"error": str(e)
}
overall_healthy = False
# Update overall status
if not overall_healthy:
health_status["status"] = "unhealthy"
status_code = 200 if overall_healthy else 503
return JSONResponse(
status_code=status_code,
content=health_status
)
@app.get("/health/ready")
async def readiness_check() -> JSONResponse:
"""Kubernetes readiness probe endpoint."""
try:
# Test critical functionality
db_health = await db_provider.health_check()
if db_health["status"] != "healthy":
raise HTTPException(status_code=503, detail="Database not ready")
return JSONResponse(
status_code=200,
content={"status": "ready"}
)
except Exception as e:
logger.error(f"Readiness check failed: {e}")
raise HTTPException(status_code=503, detail="Service not ready")
@app.get("/health/live")
async def liveness_check() -> JSONResponse:
"""Kubernetes liveness probe endpoint."""
return JSONResponse(
status_code=200,
content={"status": "alive"}
)
logger.info("Health check endpoints configured")
🧪 Testen Ihres MCP-Servers
Lokales Testen
-
Starten Sie den MCP-Server:
# Activate virtual environment source mcp-env/bin/activate # macOS/Linux # mcp-env\Scripts\activate # Windows # Start server cd mcp_server python sales_analysis.py -
Testen Sie die Gesundheitsendpunkte:
# Basic health check curl http://localhost:8000/health # Detailed health check curl http://localhost:8000/health/detailed -
Testen Sie MCP-Tools:
# List available tools curl -X POST http://localhost:8000/mcp \ -H "Content-Type: application/json" \ -H "x-rls-user-id: 00000000-0000-0000-0000-000000000000" \ -d '{"method": "tools/list", "params": {}}' # Get table schemas curl -X POST http://localhost:8000/mcp \ -H "Content-Type: application/json" \ -H "x-rls-user-id: 00000000-0000-0000-0000-000000000000" \ -d '{ "method": "tools/call", "params": { "name": "get_multiple_table_schemas", "arguments": { "table_names": ["retail.stores", "retail.products"] } } }'
Integrationstests mit VS Code
-
Konfigurieren Sie VS Code MCP:
// .vscode/mcp.json { "servers": { "zava-retail-test": { "url": "http://127.0.0.1:8000/mcp", "type": "http", "headers": {"x-rls-user-id": "00000000-0000-0000-0000-000000000000"} } } } -
Testen Sie im KI-Chat:
- Öffnen Sie den KI-Chat in VS Code
- Geben Sie
#zavaein und wählen Sie Ihren Server aus - Fragen Sie: "Welche Tabellen sind verfügbar?"
- Fragen Sie: "Zeigen Sie mir die Top 5 Geschäfte nach Anzahl der Bestellungen"
Unit-Tests
Erstellen Sie umfassende Unit-Tests:
# tests/test_mcp_server.py
import pytest
import asyncio
from mcp_server.sales_analysis_postgres import PostgreSQLSchemaProvider
from mcp_server.config import config
@pytest.mark.asyncio
async def test_database_connection():
"""Test database connectivity."""
db = PostgreSQLSchemaProvider()
try:
await db.create_pool()
health = await db.health_check()
assert health["status"] == "healthy"
finally:
await db.close_pool()
@pytest.mark.asyncio
async def test_table_schema_retrieval():
"""Test table schema retrieval."""
db = PostgreSQLSchemaProvider()
try:
await db.create_pool()
schema = await db.get_table_schema("retail.stores", "00000000-0000-0000-0000-000000000000")
assert schema["table_name"] == "retail.stores"
assert len(schema["columns"]) > 0
finally:
await db.close_pool()
@pytest.mark.asyncio
async def test_query_execution():
"""Test query execution with RLS."""
db = PostgreSQLSchemaProvider()
try:
await db.create_pool()
result = await db.execute_query(
"SELECT COUNT(*) as store_count FROM retail.stores",
"00000000-0000-0000-0000-000000000000"
)
assert "store_count" in result
finally:
await db.close_pool()
🎯 Wichtige Erkenntnisse
Nach Abschluss dieses Labs sollten Sie Folgendes erreicht haben:
✅ Funktionsfähiger MCP-Server: FastMCP-Server mit Datenbankintegration
✅ Konfigurationsmanagement: Robuste, umgebungsbasierte Konfiguration
✅ Datenbankschicht: PostgreSQL-Integration mit Connection-Pooling
✅ MCP-Tools: Tools für Schema-Analyse und Abfrageausführung
✅ RLS-Integration: Kontextmanagement für Row Level Security
✅ Überwachung: Umfassende Endpunkte für Gesundheitschecks
✅ Teststrategie: Lokales Testen und Integration mit VS Code
🚀 Was kommt als Nächstes?
Fahren Sie mit Lab 06: Tool-Entwicklung fort, um:
- Ihre MCP-Tool-Sammlung zu erweitern
- Fortgeschrittene Abfragemuster zu implementieren
- Datenvalidierung und -transformation hinzuzufügen
- Spezialisierte Analysetools zu erstellen
📚 Zusätzliche Ressourcen
FastMCP-Framework
- FastMCP-Dokumentation - Offizieller FastMCP-Leitfaden
- MCP-Spezifikation - Protokollspezifikation
- Leitfaden zur Tool-Entwicklung - Erstellung von MCP-Tools
Datenbankintegration
- asyncpg-Dokumentation - PostgreSQL-Async-Treiber
- Best Practices für Connection-Pooling - PostgreSQL-Optimierung
- Leitfaden zur Row Level Security - RLS-Implementierung
FastAPI-Muster
- FastAPI-Dokumentation - Referenz für das Web-Framework
- Dependency Injection - FastAPI-Muster
- Hintergrundaufgaben - Verwaltung von asynchronen Aufgaben
Weiter: Bereit, Ihre Tools zu erweitern? Fahren Sie fort mit Lab 06: Tool-Entwicklung
Haftungsausschluss:
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