Ai-Agents

The Model Context Protocol gives LLMs a standard way to call external tools, read files, and query databases. You skip the rewrite each time you switch models. You can build a working MCP server in Python with the official mcp SDK in under 100 lines. It runs with Claude Desktop or Claude Code in minutes. This guide walks the full path, from a tiny first server to production.

What MCP Is and Why It Changes Tool Use

MCP is a JSON-RPC 2.0 protocol. It lets an LLM client (like Claude Desktop , Claude Code, or Cursor) find and call tools exposed by a server process. The big shift from older function-calling is the discovery step. Instead of hard-coding tool defs into every prompt, the client sends a tools/list request when it connects. It gets back the full schema for everything the server exposes. Add a new tool, restart the server, and any client sees it on the next connect.

Five open source repositories dropped in March 2026 that expand what Claude Code can do. Karpathy’s AutoResearch runs overnight ML experiments without you. OpenSpace makes agent skills fix and improve themselves. CLI-Anything turns GUI software into agent-ready command-line tools. Claude Peers MCP lets many Claude Code sessions coordinate on one machine. And Google Workspace CLI opens Gmail, Drive, Calendar, and Sheets to agents. All five are free, open source, and plug right into Claude Code.

Software teams keep adding AI coding agents to their workflow. One popular trend: drop a repo-level context file, often named AGENTS.md or CLAUDE.md, to guide the agent. The idea sounds clean. Give the AI a map of the codebase and a few rules, and it should solve tasks faster.

But does it work? A new paper, “Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?” , says no. The results push back hard on the default advice.