Model Context Protocol
MCP is an open protocol that standardizes how applications provide context to LLMs
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.
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
Core Architecture
MCP Host
The application that incorporates an LLM and wants to leverage external data/tools. Examples include AI assistants, IDEs, and custom chatbots.
MCP Client
A library instance running inside the host that maintains 1:1 connections to MCP servers, handling requests and data on behalf of the host/LLM.
MCP Server
External services that expose capabilities through the MCP interface, wrapping data sources like Google Drive, Slack, or databases.
Data Sources
Local resources and remote services that are uniformly accessed through MCP, making file systems and web APIs feel the same to the LLM.
About
Modern AI assistants historically faced an integration challenge: each new tool or data source required a custom integration, leading to a combinatorial explosion of connectors. This can be thought of as an M×N integration problem, where M AI applications each needed to integrate with N external systems, resulting in M×N bespoke interfaces.
MCP was created to simplify this into an M+N problem – tool providers implement N standardized MCP servers (one per system), and application developers implement M MCP clients (one per AI application). Every client-server pair speaks the same protocol, so any AI app can interface with any tool that adheres to MCP.
Before vs. After MCP: On the left, an LLM must use distinct, custom APIs to connect to each external service (Slack, Google Drive, GitHub, etc.), resulting in many redundant integrations. On the right, MCP introduces a unified API layer between the LLM and external systems, so the LLM connects through MCP and can access all services via the standard protocol.
Anthropic open-sourced MCP in November 2024, and it has since grown into a community-driven effort with broad industry interest. The motivation was not only to improve model capabilities by providing them relevant context, but also to establish a sustainable integration architecture for AI systems.
Instead of each AI vendor or team maintaining dozens of connectors, developers can contribute to or use the standard MCP connectors. As Anthropic's CTO put it, open standards like MCP act as "bridges that connect AI to real-world applications", forming the foundation for more agentic systems where AI assistants can reliably interact with external information and services.