How to Build a Production-Ready MCP Server for Django Apps

The Model Context Protocol (MCP) has rapidly gained traction among AI-focused development teams as a standardised way to expose application capabilities to AI clients like Claude or custom agents. Developers at one firm spent several months integrating MCP servers into real-world Django codebases, documenting practical decisions rather than beginner tutorials. The MCP server runs as a standalone Python process that imports Django models and services, using the official MCP Python SDK with JSON-RPC over stdio or HTTP/SSE transport. Crucially, MCP handles only the protocol layer — security, access control, rate limiting, and business logic must still be implemented separately by the development team. The approach offers the most value to teams building multiple agents or wanting internal tools to work seamlessly with off-the-shelf AI clients through a single, unified server.