About Model Context Protocol

Model Context Protocol (MCP) is an open standard developed by Anthropic that enables seamless integration between AI models and external data sources, tools, and services. It provides a standardized way for AI models like Claude to communicate with various tools, APIs, and data sources, enabling more powerful and flexible AI applications.

MCP was created to solve the "M×N problem" in AI development—the challenge of connecting multiple AI models (M) with numerous external tools and data sources (N). Without a standard protocol, each integration requires custom development work, creating an exponentially complex landscape of connections as both models and tools multiply.

With MCP, developers can create tools and services that work seamlessly with compatible AI models, allowing for consistent, reliable interactions between AI agents and external systems. This creates a more interoperable and accessible AI ecosystem for everyone, from individual developers to enterprise organizations.

An MCP server is a program that implements the Model Context Protocol, acting as a bridge between AI models and external systems. These servers expose specific capabilities—like file access, database connections, or API integrations—to AI models in a secure, standardized way.

Think of an MCP server as a translator and gatekeeper: it understands both what the AI model needs and how to safely interact with external systems to fulfill those needs. For example, an MCP server might allow an AI assistant to query a weather API, search your email, or manage files on your computer—all while maintaining appropriate security boundaries.

MCP servers can be run locally on your device or deployed to cloud environments, depending on the specific use case and security requirements. They can be as simple as a basic file access utility or as complex as an enterprise data integration hub.

MCP uses a client-server architecture where communication occurs through JSON-RPC messages. In this model:

• MCP Clients are AI applications like Claude Desktop or AI-powered development environments that need to access external tools and data.
• MCP Servers expose functionality through three key primitives:
  • Resources - structured data that can be included in an AI model's context
  • Prompts - instructions or templates that guide the AI model's behavior
  • Tools - executable functions that an AI model can call to retrieve information or perform actions

When an AI assistant needs information from an external system, it makes a request to the appropriate MCP server. The server processes this request, interacts with the external system according to its implementation, and returns the relevant data back to the AI in a structured format it can understand.

This standardized approach ensures that AI models can interact with various systems without needing custom integration code for each new connection. It also provides built-in security controls and a consistent developer experience across different integrations.

MCP servers enable a wide range of practical applications that extend the capabilities of AI assistants beyond their built-in knowledge. Some common use cases include:

• File and Document Management - Allowing AI assistants to read, write, and organize files on your computer
• Team Communication - Integrating with platforms like Slack to automate notifications and team updates
• Development Workflows - Connecting with code repositories through GitHub integration
• Data Analytics - Accessing databases and analytics platforms to generate insights
• Real-time Information - Retrieving current weather forecasts, cryptocurrency prices, or news updates

The versatility of MCP servers extends to niche applications as well, such as 3D printer management, home automation, and specialized research tools. As the ecosystem grows, we're seeing MCP servers being developed for increasingly diverse use cases.

Industry leaders like Block and Apollo have already integrated MCP into their systems, while development tools companies including Zed, Replit, Codeium, and Sourcegraph are working with MCP to enhance their platforms—enabling AI agents to better understand context around coding tasks.

The MCP Servers Directory is a community-driven project aimed at helping developers discover servers and tools that support the Model Context Protocol. Our goal is to make it easy for AI developers to find compatible services they can integrate with their applications.

This directory is maintained by volunteers who are passionate about the AI community and the potential of MCP to create a more unified ecosystem for AI agents. We believe that developers shouldn't have to reinvent the wheel when integrating AI with external systems.

All server listings in this directory are submitted by developers or community members. We verify that each server supports MCP before adding it to our listings, ensuring that they can be used with Anthropic's Claude and other compatible models.