MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

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Why MCP?

MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:

  • A growing list of pre-built integrations that your LLM can directly plug into
  • The flexibility to switch between LLM providers and vendors
  • Best practices for securing your data within your infrastructure

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General architecture

At its core, MCP follows a client-server architecture where a host application can connect to multiple servers:

  • MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP
  • MCP Clients: Protocol clients that maintain 1:1 connections with servers
  • MCP Servers: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol
  • Local Data Sources: Your computer’s files, databases, and services that MCP servers can securely access
  • Remote Services: External systems available over the internet (e.g., through APIs) that MCP servers can connect to

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Get started

Choose the path that best fits your needs:

Quickstart

Build and connect to your first MCP server

Examples

Check out our gallery of official MCP servers and implementations

Clients

View the list of clients that support MCP integrations

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Tutorials

Building a MCP client

Learn how to build your first MCP client

Building MCP with LLMs

Learn how to use LLMs like Claude to speed up your MCP development

Debugging Guide

Learn how to effectively debug MCP servers and integrations

MCP Inspector

Test and inspect your MCP servers with our interactive debugging tool

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Explore MCP

Dive deeper into MCP’s core concepts and capabilities:

Core architecture

Understand how MCP connects clients, servers, and LLMs

Resources

Expose data and content from your servers to LLMs

Prompts

Create reusable prompt templates and workflows

Tools

Enable LLMs to perform actions through your server

Sampling

Let your servers request completions from LLMs

Transports

Learn about MCP’s communication mechanism

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Contributing

Want to contribute? Check out @modelcontextprotocol on GitHub to join our growing community of developers building with MCP.

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