A comprehensive guide to building production-grade MCP (Model Context Protocol) servers that expose internal organizational data to AI assistants. Covers the full stack: project setup with TypeScript and the official MCP SDK, connecting to PostgreSQL databases, defining well-designed tools and resources, implementing bearer token and OAuth 2.0 authentication, scoping data access per user role, wrapping internal REST APIs, building a RAG tool with pgvector for document search, Dockerizing for deployment, health checks, audit logging, rate limiting, and connecting to clients like Claude Desktop. Includes concrete code examples and common pitfalls to avoid.
Table of contents
Table of ContentsPrerequisitesWhat is MCP, and Why Does It Matter for Internal Data?Architecture OverviewSetting Up the ProjectBuilding the MCP ServerAdding AuthenticationScoping Data Access Per UserConnecting to Internal APIsBuilding a RAG Tool for Internal DocumentsProduction DeploymentConnecting Your MCP Server to AI ClientsCommon PitfallsWrapping UpSort: