数据库访问服务器

# Start everything
docker-compose up -d

# Check logs
docker-compose logs -f mcp-server

# Stop everything
docker-compose down

# Install dependencies
pip install -r requirements.txt

# Set environment variables
export DB_HOST=localhost
export DB_PASSWORD=your_password
export DB_USER=readonly_user
export DB_NAME=production

# Run the server
python database_server.py

# Set your Anthropic API key
export ANTHROPIC_API_KEY=your_key_here

# Run in interactive mode
python agent_app.py

# Run demo queries
python agent_app.py demo

# Use Docker MCP server instead of local
export MCP_SERVER_MODE=docker
python agent_app.py

# Install Inspector
npm install -g @modelcontextprotocol/inspector

# Test your server
mcp-inspector python database_server.py

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=your_langsmith_key
export LANGCHAIN_PROJECT=mcp-database-agent

python agent_app.py

docker build -t mcp-database-server .

docker run -d \
  --name mcp-database \
  --memory=512m \
  --cpus=1.0 \
  --security-opt seccomp=default.json \
  --cap-drop=ALL \
  --read-only \
  --tmpfs /tmp \
  -e DB_HOST=your-db-host \
  -e DB_PASSWORD=secure_password \
  mcp-database-server

# Check container health
docker inspect --format='{{.State.Health.Status}}' mcp-database

# View logs
docker logs -f mcp-database

# View audit logs (if volume mounted)
docker exec mcp-database cat /var/log/mcp/server.log

@mcp.tool()
async def your_tool_name(param: str) -> dict:
    """
    Clear description for the AI.
    
    Args:
        param: Description of the parameter
    """
    # Your implementation
    return {"result": "data"}

@mcp.resource("custom://your-resource")
async def your_resource() -> str:
    """Description of what this resource provides."""
    data = {"key": "value"}
    return json.dumps(data, indent=2)