The Challenge: Giving AI the Keys to Your Database

Giving a Large Language Model direct access to your database is a security nightmare. Sound familiar?

You want AI agents to answer questions and perform tasks using your real-world data. But the risk of SQL injection, data leaks, or a rogue query taking down your production server is immense. Most solutions are either too locked-down to be useful or so open they’re an engraved invitation for disaster.

The real problem isn’t the AI—it’s the lack of a secure, intelligent gatekeeper.

The Playbook: A Secure Bridge for AI and Data

You don’t need to choose between a powerful AI and a secure database. You need a better system.

This project delivers a production-ready playbook: a serverless MCP (Model Context Protocol) server that acts as a secure intermediary between AI agents and a MySQL database. It’s built on a foundation of zero-trust security and scalable, modern infrastructure.

Here’s the 3-step framework that makes it work.

1. The Architecture: Serverless, Stateful, and Built for Speed

To handle requests from anywhere in the world with minimal latency, the entire system is built on Cloudflare’s serverless edge network.

• Cloudflare Workers run the core logic. Scalable, fast, no servers to manage.
• Durable Objects solve the state problem. Each user session gets a persistent, isolated connection to the database, something normally impossible in a stateless serverless world.
• Hyperdrive provides a pooled connection to the MySQL database, preventing the server from being overwhelmed.

Think of it as an intelligent global routing system for your AI’s data requests—fast, resilient, and efficient.

2. The Security Model: A Multi-Layered Defense

Here’s what matters. This isn’t just a simple API. It’s a fortress.

• Step 1: Locked-Down Access. A dual authentication system covers all bases. A robust OAuth 2.1 flow with PKCE for user-facing applications (like IDEs), and a simple, secure API Key system for server-to-server communication.
• Step 2: An Anti-SQLi Engine. This is the core of the defense. We go beyond simple pattern matching.
  1. Blocklist: First, we reject obvious attack patterns (DROP, INSERT, UNION SELECT, etc.).
  2. AST Parsing: Then, the magic. We parse every incoming query into an Abstract Syntax Tree (AST)—its fundamental grammatical structure. If the intent of the query is malicious, we block it, even if it’s cleverly disguised.

It’s like having a security guard who not only checks IDs but also understands 20 different languages to detect subtle threats.

• Step 3: Sensible Guardrails. The system prevents accidents before they happen. Queries have timeouts, LIMIT clauses are automatically added to prevent massive data dumps, and access can be restricted to specific databases or tables.

Proof Kit: Receipts and Results

This isn’t theory. Here’s the proof that it works.

1. Production-Ready, Not a Prototype The template is a complete, deployable package. It includes:

• Dockerized local environment for rapid, consistent testing.
• One-command deployment via Cloudflare’s wrangler.jsonc.
• Integrated monitoring with Sentry for production error tracking.

2. AI Building AI: The Meta-Narrative Here’s the fascinating part: this project wasn’t just built for AI; it was built with an AI partner. The entire development process, from initial requirements in PRPs/ to implementation patterns, was a collaboration between human and machine—a testament to the future of software development.

3. Extensible by Design The architectural patterns are so solid that it allowed extending the same model to Salesforce, proving it’s not a one-trick pony. The principles can be adapted for any data source.

The Bottom Line

This project provides a clear blueprint for solving one of the biggest challenges in the AI era: connecting models to valuable, private data without compromising on security. It demonstrates a security-first, scalable, and production-ready approach that any organization can adopt.