Connect Your AI Agent to Anything: Building MCP Servers with Python and FastMCP

Connect Your AI Agent to Anything: Building MCP Servers with Python and FastMCP The problem with local AI agents isn't intelligence — it's isolation. Your LLM can reason about code, but it can't run it. It can discuss your database, but it can't query it. It's a brain in a jar. The Model Context Protocol (MCP) , developed by Anthropic, solves this. It's a standard that lets AI agents connect to external tools — filesystems, APIs, databases, CLIs — through a consistent interface. Build an MCP server, and any MCP-compatible client (Claude, a local agent, your own code) can use your tool. FastMCP is the Python library that makes building MCP servers straightforward. Here's how it works. What MCP Actually Is MCP defines a standard protocol between: MCP Client : the AI agent/assistant making requests (e.g., Claude Desktop, your agent code) MCP Server : a service that exposes tools the agent can call When an agent needs to do something real — read a file, query an API, run a command — it cal