What is MCP (Model Context Protocol)?

MCP (Model Context Protocol) is an open-source standard created by Anthropic that enables AI applications like Claude and ChatGPT to connect to external tools, data sources, and services. Think of it like a USB-C port for AI—a universal connector that lets AI models plug into various tools and databases in a consistent, standardized way.

MCP works through three main components: MCP Hosts (AI applications like Claude Desktop), MCP Clients (connectors within the host that communicate with servers), and MCP Servers (external services that provide data and tools). When you ask an AI to perform a task requiring external data, the client discovers available tools, sends requests to the appropriate server, and returns results to the AI.

What can MCP servers do?

MCP servers can connect AI to virtually any external system. Common capabilities include: accessing databases and file systems, integrating with tools like GitHub, Slack, and Notion, executing code and running tests, managing cloud infrastructure, scraping web data, and automating workflows. Each server exposes specific tools the AI can use during conversations.

MCP is used by developers, enterprises, and AI enthusiasts who want to extend AI capabilities. Major AI providers including OpenAI, Google DeepMind, and Anthropic support MCP. It's particularly popular among developers building AI agents, companies creating enterprise knowledge assistants, and teams automating complex workflows.

How do I install an MCP server?

MCP servers can be installed in AI applications like Claude Desktop or Cursor. Typically, you add the server configuration to your app's settings file (like claude_desktop_config.json), specifying the server command and any required environment variables. Many servers are available via npm or can be run directly from their GitHub repositories.

MCP includes built-in security features like authentication specs and permission controls. However, as with any integration, you should review server code before installation, use environment variables for sensitive credentials, only install servers from trusted sources, and follow the principle of least privilege when granting permissions.

What's the difference between MCP and regular APIs?

While MCP uses APIs under the hood, it's specifically designed for AI context. Regular APIs serve general web applications, while MCP provides a standardized interface that AI models can discover and use autonomously. MCP servers are tool-agnostic, schema-driven, and reusable across different AI agents without custom integration code.

Can I build my own MCP server?

Yes! MCP provides SDKs in TypeScript, Python, C#, Java, and other languages to help you build custom servers. You can create servers that expose your own tools, connect to proprietary systems, or wrap existing APIs in an MCP-compatible interface. The official documentation provides guides and examples to get started.

Discover Top MCP Servers

Official MCP Servers

View all official servers

Bright Data

2,033

Empowers AI agents to access, discover, and extract real-time web data, bypassing restrictions and bot detection.

Scout Monitoring

Provide AI Assistants with real-time application performance and error data for targeted code analysis and fixes.

Firecrawl

4,195

Empowers LLMs with advanced web scraping capabilities for content extraction, crawling, and search functionalities.

API Development

ElevenLabs

1,214

Enables interaction with Text to Speech and audio processing APIs for MCP clients.

API Development

Magic

4,271

Generate modern UI components instantly from natural language descriptions within your IDE.