MCP Overview
Model Context Protocol (MCP) explained: the open standard for connecting AI models to tools, data sources, and external systems.
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MCP Protocol Deep Dive
Model Context Protocol — the "USB-C of tools." Open standard for giving LLMs access to tools, data, and prompts via a unified JSON-RPC interface.
MCP solves the N×M integration problem: without it, every LLM client needs a custom connector for every tool/data source. With MCP, every tool speaks one protocol, and every client consumes it identically.
Adoption (2026): Over 97 million monthly SDK downloads. 13,000+ MCP servers on GitHub. Adopted by OpenAI, Google DeepMind, Microsoft, and all major agent frameworks. Anthropic donated MCP to the Linux Foundation's Agentic AI Foundation in December 2025. Protocol version is date-string versioned (e.g., 2025-11-25) and negotiated during the initialize handshake.
MCP vs. Function Calling: Different Layers
| Concept | Function Calling (Phase 1) | MCP (Phase 2) |
|---|---|---|
| What it does | LLM generates structured JSON specifying which function to call with what args | Standardized infrastructure for how tools are discovered, invoked, and managed |
| Who defines it | Each LLM provider (Claude, GPT, Gemini) has its own format | Open standard — any client speaks to any server |
| Scope | Single API call: "I want to call tool X with args Y" | Full lifecycle: discovery → auth → invocation → result → monitoring |
| Analogy | SQL query (the intent) | ODBC/JDBC driver (the connection layer) |