Model Context Protocol (MCP)
Model Context Protocol (MCP) is Anthropic's open standard for connecting AI assistants to external data sources and tools — letting any compliant client use any compliant server's capabilities.
MCP, introduced by Anthropic in late 2024 and broadly adopted by 2026, formalises a JSON-RPC interface between LLM clients (Claude Desktop, Cursor, Codex, Cline, custom agents) and servers that expose tools, resources, and prompt templates. The win is reusability: write one MCP server for filesystem, GitHub, Postgres, internal API, and every compliant client can use it. OpenAI, Anthropic, Google, Cursor, Continue, Zed all ship MCP-compatible clients. The pattern composes with function calling — the server defines the tools, the client routes through MCP, the model emits function calls.
When to use model context protocol (mcp)
- Exposing internal capabilities to multiple LLM clients.
- Avoiding vendor lock-in on tool integration.
- Distributing capabilities as reusable packages.
Common mistakes
- Confusing MCP with function calling — MCP is the protocol, function calling is the mechanism.
- Shipping MCP servers without auth or quotas — same security concerns as any RPC endpoint.
FAQ
What is model context protocol (mcp)?
Model Context Protocol (MCP) is Anthropic's open standard for connecting AI assistants to external data sources and tools — letting any compliant client use any compliant server's capabilities.
When should I use model context protocol (mcp)?
Exposing internal capabilities to multiple LLM clients. Avoiding vendor lock-in on tool integration. Distributing capabilities as reusable packages.
What are the most common mistakes with model context protocol (mcp)?
Confusing MCP with function calling — MCP is the protocol, function calling is the mechanism. Shipping MCP servers without auth or quotas — same security concerns as any RPC endpoint.
Related terms
- MCP (Model Context Protocol) — MCP is an open protocol from Anthropic that standardises how language models connect to external tools, data sources, and prompts — the USB-C of LLM integrations.
- Function calling (tool use) — Function calling lets a language model emit a structured request to invoke a developer-defined tool, enabling reliable JSON output and agent workflows.
- Tool use (LLM) — Tool use is the umbrella term for any LLM mechanism that lets the model invoke external functions, APIs, or services — function calling, code interpreter, MCP servers, browser actions.
- AI agent — An AI agent is a system where a language model autonomously plans and executes a sequence of tool calls to accomplish a goal.
Sources
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/model-context-protocol.md.