Building a Quantum-Enhanced API Gateway: MCP Secure Gateway

This week at API Days Paris 2025, I sat through fascinating talks about MCP (Model Context Protocol), quantum-enhanced security, and API reliability patterns. The conversations kept circling back to one question: how do we actually implement these concepts in production?

So I built MCP Secure Gateway to answer that question.

What is MCP Secure Gateway?

It's a production-ready API gateway that demonstrates:

• Quantum Random Number Generation (QRNG) for cryptographically secure tokens
• MCP Protocol implementation for AI agent interactions
• Enterprise-grade authentication, rate limiting, and monitoring
• API Drift Detection through contract testing

Built with FastAPI, it serves as both a working reference implementation and a learning resource.

The Quantum Security Angle

Traditional random number generators use pseudo-random algorithms. They're deterministic—given the same seed, you get the same sequence. For JWT token IDs and cryptographic operations, that's a potential vulnerability.

Enter QRNG

Quantum Random Number Generators leverage quantum mechanics (like vacuum state fluctuations) to produce truly random numbers. MCP Secure Gateway integrates three providers:

1. ANU (Australian National University)

• Free, public quantum RNG
• Perfect for development and testing
• Uses vacuum fluctuations in a quantum beam splitter

2. Quantinuum (Enterprise)

• Commercial quantum computing platform
• High throughput, API-based access
• Requires API key

3. ID Quantique (Hardware-based)

• Physical quantum RNG devices
• Banking-grade security
• Requires API key

The Implementation

class QRNGService:
    async def get_random_bytes(self, length: int = 32) -> bytes:
        """Get cryptographically secure random bytes"""
        try:
            if self.current_provider == QRNGProvider.ANU:
                return await self._get_anu_bytes(length)
            elif self.current_provider == QRNGProvider.QUANTINUUM:
                return await self._get_quantinuum_bytes(length)
            elif self.current_provider == QRNGProvider.IDQ:
                return await self._get_idq_bytes(length)
        except Exception as e:
            # Graceful fallback to classical cryptography
            if settings.QRNG_FALLBACK_ENABLED:
                return secrets.token_bytes(length)
            raise

Key design decision: Automatic fallback to secrets.token_bytes() ensures the system never fails due to quantum provider unavailability. Security degrades gracefully rather than catastrophically.

JWT with Quantum-Backed Token IDs

Every JWT gets a unique jti (JWT Token ID) generated from quantum randomness:

async def create_token(self, user_id: str, token_type: str = "access") -> str:
    # Generate quantum-backed JWT ID
    jti = await qrng_service.generate_token_id()

    payload = {
        "sub": user_id,
        "jti": jti,  # Quantum-backed unique identifier
        "exp": expire,
        "iat": now,
        "type": token_type
    }

    return jwt.encode(payload, self.private_key, algorithm=self.algorithm)

This makes token prediction and collision attacks exponentially harder.

MCP Protocol Integration

MCP (Model Context Protocol) is becoming the standard for AI agent communication. The gateway implements core MCP patterns:

Tool Discovery

@router.get("/mcp/tools")
async def list_tools():
    """List available MCP tools"""
    return [
        {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                }
            }
        },
        # ... more tools
    ]

Tool Execution

@router.post("/mcp/execute")
async def execute_tool(tool_call: MCPToolCall):
    """Execute an MCP tool"""
    if tool_call.tool == "get_weather":
        return MCPResponse(
            success=True,
            result={
                "location": tool_call.arguments["location"],
                "temperature": 22,
                "condition": "Sunny"
            }
        )

This pattern allows AI agents to discover and use tools dynamically, with proper authentication and rate limiting.

Production-Ready Features

1. Structured Logging

Every request gets a correlation ID for tracing:

@app.middleware("http")
async def log_requests(request: Request, call_next):
    correlation_id = request.headers.get(
        "X-Correlation-ID", 
        f"req-{int(time.time() * 1000)}"
    )

    logger.info(
        "request_started",
        method=request.method,
        path=request.url.path,
        correlation_id=correlation_id
    )

    response = await call_next(request)
    response.headers["X-Correlation-ID"] = correlation_id
    return response

2. Health Checks

Kubernetes-ready liveness and readiness probes:

@router.get("/health")
async def health_check():
    return {
        "status": "healthy",
        "version": "1.0.0",
        "qrng": qrng_service.get_provider_status(),
        "features": {
            "quantum_auth": True,
            "mcp_streaming": True,
            "rate_limiting": True
        }
    }

3. Docker Compose Stack

Full observability stack included:

services:
  gateway:
    build: .
    ports:
      - "8000:8000"
    depends_on:
      - redis
      - prometheus

  redis:
    image: redis:7-alpine

  prometheus:
    image: prom/prometheus

  grafana:
    image: grafana/grafana

Lessons from API Days Paris 2025

Three key insights shaped this project:

1. "Don't Overfit Your Problem"

I kept the architecture simple:

• No over-engineered microservices
• Straightforward FastAPI structure
• Clear separation of concerns
• Easy to understand and extend

2. "If You Have a Terrible API, You'll End Up with a Terrible MCP"

MCP doesn't fix bad API design. The gateway focuses on:

• Clear, RESTful endpoints
• Consistent error handling
• Proper HTTP status codes
• Comprehensive documentation

3. API Drift is Real

Contract testing catches breaking changes:

def test_auth_endpoint_contract():
    """Ensure auth endpoint maintains contract"""
    response = client.post(
        "/auth/login",
        json={"username": "demo", "password": "demo123"}
    )

    assert response.status_code == 200
    data = response.json()

    # Contract assertions
    assert "access_token" in data
    assert "refresh_token" in data
    assert "token_type" in data
    assert data["token_type"] == "bearer"

Getting Started

# Clone the repository
git clone https://github.com/YOUR_USERNAME/mcp-secure-gateway.git
cd mcp-secure-gateway

# Setup environment
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Configure
cp .env.example .env

# Run with Docker
docker-compose up -d

# Or run locally
uvicorn app.main:app --reload

Visit http://localhost:8000/docs for interactive API documentation.

Testing

# Run all tests
pytest

# With coverage
pytest --cov=app --cov-report=html

# Contract tests only
pytest tests/contract/ -v

What's Next?

This is v1.0 - a foundation. I'm considering:

1. Rate limiting with Redis - Token bucket algorithm
2. Token revocation list - Redis-backed JWT blacklist
3. API key management - Multi-tenant support
4. More MCP tools - File operations, database queries
5. Grafana dashboards - Pre-configured monitoring

Why Build This?

As a Solutions Architect, I've seen too many "production-ready" examples that are anything but. They skip error handling, ignore observability, and assume perfect network conditions.

Resources

• GitHub Repository
• MCP Specification
• ANU QRNG
• API Days Paris

Let's Connect

Building something similar? Have questions about quantum security or MCP? Find me on:
GitHub
LinkedIn

Built with ❤️ in Paris | Inspired by API Days 2025

Tags: #api #security #quantum #python #fastapi #mcp #devops #architecture