What is Model Context Protocol?
Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). Think of MCP like a USB-C port for AI applications - it provides a standardized way to connect AI models to different data sources and tools, allowing them to work together seamlessly.
Developed by Anthropic and released as an open standard, MCP solves a critical problem in AI development: connecting AI models with the data and tools they need to be truly effective. Instead of requiring custom integrations for each data source, MCP provides a universal interface that works across different AI systems and data repositories.
Key Features
Universal Connectivity
Connect any AI model to any data source or tool using a standardized protocol, eliminating the need for custom integrations.
Secure Architecture
Built with security in mind - data sources control access to their information, and there's no need to share API keys with LLM providers.
Tool Integration
Enable AI models to use external tools and services through a standardized interface for actions like file operations, API calls, and more.
Open Standard
As an open protocol, MCP can be implemented by any organization or developer, fostering a collaborative ecosystem.
How MCP Works
MCP follows a client-server architecture where MCP clients (AI applications) connect to MCP servers (data sources and tools). The protocol defines how these components communicate, allowing AI models to access information and functionality from external systems.
This separation of concerns means data providers maintain control over their information, while AI applications gain the ability to work with a wide range of tools and data sources through a consistent interface.
Learn More About MCP Architecture// Example MCP client-server interaction
const mcpClient = new MCPClient();
// Connect to an MCP server
await mcpClient.connect({
transport: "stdio",
command: "npx",
args: ["@modelcontextprotocol/server-filesystem"]
});
// List available tools
const tools = await mcpClient.listTools();
// Use a tool from the server
const result = await mcpClient.callTool({
name: "readFile",
parameters: { path: "/path/to/file.txt" }
});Getting Started with MCP
Ready to start building with MCP? Whether you're developing an AI application that needs to access external data, or creating an MCP server to expose your data to AI systems, our documentation will help you get started quickly.
Visit our Getting Started guide to learn how to install and use MCP in your projects.