The Model Context Protocol

Understanding the standard that enables AI models to interact with the real world.

What is MCP Store?

McpSvr Store serves as a central hub for discovering MCP server and client implementations. Our goals are to:

  • Provide a curated list of high-quality MCP servers and clients
  • Help developers find the right tools for their AI applications
  • Foster the MCP ecosystem and community
  • Share best practices and implementation patterns

Frequently Asked Questions

What is Model Context Protocol?

Model Context Protocol (MCP) is an open protocol that enables AI models to securely interact with local and remote resources through standardized server implementations. This protocol bridges the gap between AI models and external tools, allowing for seamless integration of various capabilities. Just like the USB-C port to the computers, it is a standard that allows for the connection of different tools or services to the AI models.

How does MCP work?

MCP works by defining a standard communication protocol between AI models and tool servers. When an AI model needs to access external functionality (like searching the web, manipulating files, or calling APIs), it communicates with an MCP server that handles the actual execution of these tools. The model sends requests to the server, which returns the results back to the model.

Why is MCP important?

MCP is important because it provides a standardized way for AI models to interact with the world beyond their context window. It enables AI systems to leverage external tools and data sources while maintaining security boundaries. MCP helps solve the problem of tool integration for AI systems in a consistent, scalable way.

Who created MCP?

The Model Context Protocol was initially developed by Anthropic, the creator of Claude AI. However, it is designed as an open protocol that anyone can implement and extend, fostering a collaborative ecosystem around AI tool integration.

What types of tools can be integrated with MCP?

MCP can integrate virtually any type of tool that can be exposed through an API, including: web search engines, database queries, file system operations, code execution environments, API calls to external services, mathematical computation tools, and custom domain-specific tools.

What are the benefits of using MCP?

Benefits include: standardized integration between AI models and tools, improved security through defined boundaries, extensibility for custom tools and use cases, interoperability across different AI providers, reduced development effort through shared implementations, and enabling AI models to perform more complex, real-world tasks.

How does MCP enhance AI security?

MCP enhances security by creating clear boundaries between AI models and external tools. Models can only interact with the world through explicitly defined tool interfaces, and MCP servers can implement permission systems, rate limiting, and audit logging. This helps prevent unintended consequences and provides better oversight of AI capabilities.

How can I implement MCP in my applications?

To implement MCP, you can either use existing MCP server implementations listed on this site, or create your own custom implementation following the protocol specification. You'll need to define the tools you want to expose, implement the server-side logic, and connect it to your AI model provider that supports MCP (like Anthropic's Claude).