ModelRefs / MCP (Model Context Protocol) — AI Glossary

MCP (Model Context Protocol) — AI Glossary

An open protocol from Anthropic for standardizing how LLM clients connect to external tools, data sources, and prompts. Also called Model Context Protocol.

Overview

MCP defines a client-server architecture where MCP servers expose resources, tools, and prompts that any MCP-compatible client (Claude, Cursor, ChatGPT, Zed) can discover and invoke. It plays the same role for AI as LSP plays for editors — replacing bespoke plugin integrations.

Reference details

Topicagents
Also known asModel Context Protocol
Last reviewed2026-06-24

How it works

  1. Client and server exchange JSON-RPC messages over a transport that is either a local process's standard streams or an HTTP connection.
  2. The session opens with a capability negotiation, so each side learns what the other supports before any tool is listed and a newer client can talk to an older server.
  3. The client then asks the server to list what it offers, and those returned descriptions and schemas are what the client injects into the model's tool definitions.
  4. The model therefore never speaks the protocol: it emits an ordinary tool call, and the client translates that into a protocol invocation on its behalf.
  5. Servers run as separate processes holding their own credentials, which is what makes a server a trust boundary — the client grants it access by starting it, not per call.
  6. Because discovery happens at connection time rather than at build time, a server that changes what it exposes changes what the model can do without any client being rebuilt.

Example: N plus M instead of N times M

Four clients each needing five integrations is twenty bespoke connectors. With a shared protocol it is four clients and five servers — nine components, and a new server works with every client immediately. That arithmetic is the entire argument for standardising the interface.

bespoke : 4 clients x 5 tools = 20 integrations
protocol: 4 clients + 5 servers =  9 components

Commonly confused with

MCP is not function calling and does not replace it. Function calling is how a model expresses an intent to call a tool; MCP is how a client discovers and connects to the tools available. They operate at different layers and are normally used together.

When to use it

Reach for it when:

  • The same tools must be reachable from several clients
  • You want tool discovery rather than hard-coded integrations
  • Tools are maintained separately from the applications using them

Reach for something else when:

  • One client, two tools — direct integration is simpler
  • You have not scoped what an MCP server exposes; it is a new trust boundary
  • The tool surface changes rarely enough that discovery adds nothing

Referenced by

This term is used by the following ModelRefs references:

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to MCP (Model Context Protocol) — AI Glossary.

Frequently asked questions

What is MCP (Model Context Protocol)?

An open protocol from Anthropic for standardizing how LLM clients connect to external tools, data sources, and prompts.

Is MCP (Model Context Protocol) the same as Model Context Protocol?

Yes — Model Context Protocol are common aliases for MCP (Model Context Protocol).

What concepts are related to MCP (Model Context Protocol)?

Closely related concepts include agent, tool use, function calling.