Building a Quantum-Enhanced API Gateway: MCP Secure Gateway

Source: Dev.to

Introduction

At API Days Paris 2025 the topics of Model Context Protocol (MCP), quantum‑enhanced security, and API reliability were front‑and‑center. To explore how these ideas can be applied in production, I created MCP Secure Gateway, a production‑ready API gateway that showcases:

• Quantum Random Number Generation (QRNG) for cryptographically secure tokens
• MCP implementation for AI‑agent interactions
• Enterprise‑grade authentication, rate limiting, and monitoring
• API drift detection via contract testing

The gateway is built with FastAPI and serves both as a reference implementation and a learning resource.

Quantum Random Number Generation

Traditional random number generators are deterministic; given the same seed they produce the same sequence, which can be a vulnerability for JWT IDs and other cryptographic operations. Quantum RNGs exploit quantum phenomena (e.g., vacuum fluctuations) to generate true randomness.

QRNG Providers

QRNG Service (Python)

# qrng_service.py
import secrets
from enum import Enum
from typing import Any

class QRNGProvider(Enum):
    ANU = "anu"
    QUANTINUUM = "quantinuum"
    IDQ = "idq"

class QRNGService:
    def __init__(self, provider: QRNGProvider):
        self.current_provider = provider

    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

Design note: The automatic fallback to secrets.token_bytes() guarantees that the system remains operational even if a quantum provider is unavailable, degrading security gracefully rather than catastrophically.

JWT Token Generation with Quantum IDs

Each JWT receives a unique jti (JWT Token ID) generated from quantum randomness, making token prediction and collision attacks exponentially harder.

# auth.py
import jwt
import time
from datetime import datetime, timedelta

class AuthService:
    def __init__(self, private_key: str, algorithm: str = "RS256"):
        self.private_key = private_key
        self.algorithm = algorithm

    async def create_token(self, user_id: str, token_type: str = "access") -> str:
        # Generate quantum‑backed JWT ID
        jti = await qrng_service.get_random_bytes(16).hex()

        now = datetime.utcnow()
        expire = now + timedelta(hours=1)

        payload = {
            "sub": user_id,
            "jti": jti,
            "exp": expire,
            "iat": now,
            "type": token_type,
        }

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

Model Context Protocol (MCP) Implementation

MCP is emerging as the standard for AI‑agent communication. The gateway provides core MCP patterns for tool discovery and execution.

# mcp_routes.py
from fastapi import APIRouter
from pydantic import BaseModel
from typing import Dict, Any

router = APIRouter()

class MCPToolCall(BaseModel):
    tool: str
    arguments: Dict[str, Any]

class MCPResponse(BaseModel):
    success: bool
    result: Dict[str, Any]

@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
    ]

@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",
            },
        )
    # Add handling for additional tools here

Request Correlation ID Middleware

Every request receives a correlation ID for end‑to‑end tracing.

# middleware.py
import time
from fastapi import Request, FastAPI
import logging

logger = logging.getLogger("gateway")

app = FastAPI()

@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",
        extra={"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

Health Check Endpoint

Kubernetes‑ready liveness and readiness probes expose service health and feature status.

# health.py
from fastapi import APIRouter

router = APIRouter()

@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,
        },
    }

Observability Stack

A minimal Docker Compose setup provides the gateway, Redis, Prometheus, and Grafana.

# docker-compose.yml
services:
  gateway:
    build: .
    ports:
      - "8000:8000"
    depends_on:
      - redis
      - prometheus

  redis:
    image: redis:7-alpine

  prometheus:
    image: prom/prometheus

  grafana:
    image: grafana/grafana

Key Insights & Architectural Decisions

1. Simplicity over over‑engineering – A straightforward FastAPI structure with clear separation of concerns makes the codebase easy to understand and extend.
2. MCP complements, not replaces, good API design – The gateway emphasizes RESTful endpoints, consistent error handling, proper HTTP status codes, and comprehensive documentation.
3. Contract testing prevents drift – Tests verify that public contracts (e.g., authentication responses) remain stable.

Example Contract Test

# tests/test_auth_contract.py
def test_auth_endpoint_contract(client):
    """Ensure auth endpoint maintains contract."""
    response = client.post(
        "/auth/login",
        json={"username": "demo", "password": "demo123"},
    )
    assert response.status_code == 200
    data = response.json()
    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

# Set up a virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env

# Run with Docker
docker-compose up -d

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

Open the interactive API docs at .

Running Tests

# All tests
pytest

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

# Contract tests only
pytest tests/contract/ -v

Current test coverage exceeds 95 %.

Future Work

• Rate limiting using a Redis‑backed token‑bucket algorithm
• Token revocation list (JWT blacklist) stored in Redis
• Multi‑tenant API key management
• Additional MCP tools (file operations, database queries)
• Pre‑configured Grafana dashboards for deeper observability

References

• GitHub Repository
• MCP Specification
• ANU Quantum RNG
• API Days Paris 2025

Built with ❤️ in Paris; inspired by API Days 2025.