Connecting Large Language Models (LLMs) to your internal data—whether it's a local SQLite database, a legacy CRM, or a private microservice—is the next frontier in AI engineering. The Model Context Protocol (MCP) by Anthropic has emerged as the standard for this connectivity. However, many developers hit a wall immediately after reading the spec. While the concept is elegant, the implementation detail—managing a stateless JSON-RPC 2.0 connection over standard input/output (stdio)—is tedious. It requires handling message correlation, error serialization, and strict buffer management. If you are writing raw JSON-RPC handlers to connect an AI agent to your database, you are wasting time on plumbing. This guide details how to bypass the protocol complexity using FastMCP , a Pythonic framework that treats MCP servers like FastAPI applications. We will build a production-ready server that grants an AI agent safe, structured access to a local order database. The Root Cause: Why Ra...
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