What is Model Context Protocol (MCP)?

Why Model Context Protocol matters

Before MCP, each AI agent platform built its own connector for every tool: a Slack integration here, a Gmail integration there, a custom internal-API connector. MCP standardizes the contract. Any MCP-compatible client can talk to any MCP server, the way any HTTP client can talk to any REST API. As of 2026, all major LLM vendors (OpenAI, Anthropic, Google) support MCP, and the registry of public MCP servers covers hundreds of tools.

How Model Context Protocol works

1. 1A tool author publishes an MCP server: a small program that describes its capabilities (resources, tools, prompts) using the MCP schema.
2. 2An AI agent platform (the MCP client) connects to the server over stdio, HTTP, or WebSocket.
3. 3The client asks the server "what can you do?" and receives a manifest of tools, data resources, and prompt templates.
4. 4When the LLM needs to use the tool, the agent platform calls the MCP server with structured arguments and receives a structured result.
5. 5The result is fed back into the LLM context, so the model can use it in its next decision.
6. 6Authentication, sandboxing, and rate limiting are handled at the MCP server boundary.

Examples

• A GitHub MCP server: list repos, read files, create issues, comment on pull requests — without the agent platform writing any GitHub-specific code.
• A SQL-database MCP server: list tables, query data, return rows — read-only by configuration.
• A filesystem MCP server: read and write files in a sandboxed directory, useful for code-generation agents.

References

• Anthropic — MCP announcement (2024)
• MCP specification

Related concepts

• AI agent

  An AI agent is a software program that uses a large language model (LLM) to autonomously plan and complete a task, combining reasoning, tool use, and memory. Unlike a one-shot prompt, an agent can break a goal into steps, call external tools or APIs, and decide what to do next based on intermediate results.

• AI agent platform

  An AI agent platform is software that lets organizations build, deploy, govern, and monitor AI agents at scale — typically with a workspace UI, multi-LLM access, knowledge bases, integrations, scheduling, and audit logging. The platform replaces the need for each team to assemble agent infrastructure from raw frameworks.

• Retrieval-Augmented Generation (RAG)

  Retrieval-Augmented Generation (RAG) is a technique that grounds a large language model in a specific corpus of documents at query time. Instead of relying only on what the model learned during training, RAG retrieves relevant passages from your data and adds them to the prompt — letting the model answer using your knowledge, current and proprietary.

• Multi-agent orchestration

  Multi-agent orchestration is the practice of chaining multiple specialized AI agents into a single workflow, where each agent has a defined role (researcher, writer, reviewer, publisher) and outputs flow from one agent to the next. The orchestrator decides the order, handles retries, and enforces guardrails between steps.