MCP Python SDK: AI-Optimized Technical Reference

Core Problem Solved

Eliminates repetitive database connector development across different AI tools (OpenAI, Claude, etc.). Build one MCP server, connect to all AI platforms through standardized protocol.

Technical Architecture

Essential Components

• MCP Servers: Expose tools/resources
• MCP Clients: Feed context to AI hosts
• AI Hosts: Claude Desktop, browser apps, mobile clients

Transport Options

Transport	Production Status	Use Case	Critical Limitations
Stdio	Development only	Claude Desktop testing	Randomly stops recognizing servers, single connection only
SSE (Server-Sent Events)	Web applications	Browser clients	CORS debugging required
HTTP	Production-ready	Scalable deployment	None - recommended for production

Configuration

Python Requirements

• Minimum: Python 3.10 (3.9 will break in mysterious ways)
• Dependencies: Pydantic, anyio, httpx
• Installation: pip install --user mcp (required for Claude Desktop)

FastMCP Implementation

@mcp.tool()
def get_user_data(user_id: int) -> dict:
    """Fetch user data from database."""
    # Type hints generate JSON schemas automatically
    return {"user_id": user_id, "name": "John Doe"}

Critical: Schema validation is strict - function returns must exactly match type hints or clients get cryptic Pydantic errors.

Production Deployment

HTTP Transport Setup

# Mount MCP inside existing FastAPI app
app.mount("/mcp", mcp_server.create_asgi_app())

Benefits:

• Horizontal scaling
• Load balancer compatibility
• SSL termination support
• Standard web infrastructure integration

Authentication

• OAuth 2.1 with PKCE: Built-in, works with Auth0 and standard providers
• Input validation: Recent versions reject malformed requests before code execution

Performance Characteristics

Resource Requirements

• Memory: Grows with concurrent connections (normal behavior)
• Performance: Depends on underlying tools - MCP won't optimize slow database queries
• Async: Uses anyio for async operations

Scaling Limitations

• Stdio transport: Single connection only - never use for production
• HTTP transport: Scales horizontally without issues

Critical Failure Modes

Common Production Issues

Error	Root Cause	Solution	Severity
ImportError: No module named mcp	Claude Desktop uses separate Python environment	pip install --user mcp	Blocks deployment
Schema validation failures	Function returns don't match type hints	Test annotations thoroughly	Runtime failures
Memory leaks with long connections	Connection pooling issues in older versions	Update SDK, close connections properly	Performance degradation
Hot reload stops working	Complex import dependency changes	Manual restart required	Development annoyance

Breaking Points

• UI breaks at 1000 spans: Makes debugging large distributed transactions impossible
• Connection pooling failures: Earlier versions leaked connections extensively
• CORS errors: SSE transport requires manual CORS configuration

Resource Requirements

Time Investment

• Initial setup: Hours (if following patterns)
• Schema debugging: Days (when type hints mismatch returns)
• Production deployment: Standard web app deployment time

Expertise Requirements

• Python async programming: Required for performance
• Web infrastructure: For HTTP transport deployment
• Protocol debugging: Optional (FastMCP handles protocol)

Decision Criteria

When to Use MCP

• Building connectors for multiple AI platforms
• Need standardized tool/resource exposure
• Want to avoid rebuilding same integrations

When to Avoid

• Single AI platform integration
• Simple one-off tools
• Performance-critical applications where overhead matters

Implementation Patterns

Database Integration

# Lifespan management prevents connection leaks
@mcp.tool()
def query_database(table: str, limit: int = 100) -> List[Dict[str, Any]]:
    try:
        return db.execute(f"SELECT * FROM {table} LIMIT {limit}")
    except Exception as e:
        raise MCPError(f"Database query failed: {e}")

Development Workflow

• Use uv run mcp dev server.py for hot reloading
• Test with MCP Inspector web UI before client integration
• Enable debug logging: logging.basicConfig(level=logging.DEBUG)

Language Ecosystem Comparison

Language	Status	Maturity	Production Readiness	Community
Python	Stable	Most mature	Production-ready	Largest
TypeScript	Stable	Good	Production-ready	Good
C#	Stable	Enterprise-grade	Production-ready	Enterprise-focused
Java	Stable	Good	Production-ready	Backend-focused
Go	Pre-release	Beta	Not recommended	Tiny
Rust	Community	Development	Not ready	Single maintainer

Security Considerations

Input Validation

• Recent versions have improved input validation
• Don't return sensitive data in tool responses
• Validate all inputs in tool functions
• Keep dependencies updated

OAuth Implementation

• PKCE support included
• Standard flows work without typical OAuth integration issues
• Compatible with major identity providers

Troubleshooting

Debug Tools

• MCP Inspector: Web UI for testing servers and viewing protocol messages
• Debug logging: Shows all protocol messages (gets verbose)
• Protocol spec: For debugging weird connection issues

Connection Problems

1. Check Python path and dependencies
2. Verify server doesn't crash on startup
3. Test with MCP Inspector before client integration
4. Enable debug logging to see protocol messages

Deployment Infrastructure

Cloud Compatibility

• AWS ECS: Standard deployment
• Google Cloud Run: Standard deployment
• Docker: Standard containerization
• Load balancers: Full compatibility with HTTP transport

Integration Points

• Mounts in existing FastAPI/Django applications
• Standard ASGI application interface
• Works with reverse proxies and SSL termination

Critical Warnings

What Documentation Doesn't Tell You

• Stdio transport reliability issues in production
• Schema validation strictness causes difficult debugging
• Earlier versions had significant connection leaking
• Complex import changes require manual dev server restarts
• Type hint mismatches cause cryptic client-side errors

Breaking Changes Risk

• SDK actively developed - check release notes before updates
• Protocol changes can break existing integrations
• Schema validation becomes stricter in updates

Resource Links

Essential References

• Official GitHub: Source and examples
• MCP Inspector: Testing tool
• Production Examples: Real-world patterns
• Protocol Specification: For debugging

Integration Guides

• Auth0 Integration: OAuth setup details
• FastMCP Tutorial: Step-by-step guide
• Claude Desktop Integration: Client setup