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What is an MCP Server?

Introduction​

In the rapidly evolving landscape of artificial intelligence, the ability of large language models (LLMs) to interact seamlessly with external data sources and tools is paramount. The Model Context Protocol (MCP), introduced by Anthropic in November 2024, addresses this need by providing a standardized framework for such integrations . This article delves into the essence of MCP servers, highlights some of the best MCP servers currently available, guides you through installing MCP in Anthropic's Claude, and presents practical examples of MCP usage.

What is a Model Context Protocol (MCP) Server?​

An MCP server is a lightweight program designed to expose specific data sources or functionalities to LLMs through the standardized Model Context Protocol. This setup enables AI models to access and interact with external systems, such as databases, APIs, or file systems, in a uniform manner. By implementing MCP servers, developers can bridge the gap between AI applications and the diverse tools or data repositories they need to function effectively.

The architecture of MCP is client-server based, where:

  • MCP Servers: Expose resources (like files or database entries), tools (functions that can be executed), and prompts (predefined templates to assist the model).​
  • MCP Clients: AI applications that connect to these servers to utilize the exposed functionalities.​

This standardized approach simplifies the integration process, allowing AI models to dynamically interact with various external systems without the need for custom connectors for each integration.

Best MCP Servers Available Now​

The MCP ecosystem has seen significant growth, with numerous servers developed to facilitate integration with popular tools and platforms. Here are some of the standout MCP servers:​

  • Google Drive MCP Server: Enables AI models to access and search files within Google Drive, facilitating seamless document retrieval and management
  • Slack MCP Server: Allows interaction with Slack workspaces, including channel management and messaging, enhancing communication capabilities
  • GitHub MCP Server: Provides integration with GitHub repositories, allowing AI models to fetch repository data, manage issues, and more
  • PostgreSQL MCP Server: Facilitates read-only access to PostgreSQL databases with schema inspection capabilities, enabling data querying and analysis
  • MongoDB MCP Server: Offers querying and analysis functionalities for MongoDB collections, aiding in data management tasks

These servers exemplify the versatility of MCP in connecting AI models with a wide array of external systems, thereby expanding their functional horizons.

How to Install MCP in Claude (Anthropic)​

Integrating MCP servers with Anthropic's Claude enhances its capabilities by allowing it to interact with external tools and data sources. Here's a step-by-step guide to setting up MCP in Claude:​

  1. Download Claude for Desktop: Begin by downloading and installing the Claude desktop application suitable for your operating system.​

  2. Install Node.js: Ensure that Node.js is installed on your system, as it's required for running certain MCP servers.​

  3. Edit Claude's Configuration: Access Claude's settings and modify the configuration file to include the desired MCP servers. For example, to add a filesystem MCP server, you would add the following configuration:

    jsonCopyEdit{
      "mcpServers": {
        "filesystem": {
          "command": "npx",
          "args": ["@modelcontextprotocol/server-filesystem"]
        }
      }
    }

Ensure that the paths and commands are correctly specified based on your system's setup.

  1. Restart Claude: After updating the configuration, restart the Claude application to apply the changes.​

By following these steps, Claude will be equipped to utilize the specified MCP servers, thereby extending its functionality to interact with external systems

Examples of Using MCP​

The implementation of MCP servers unlocks a multitude of practical applications. Here are some examples:​

  • Automated Document Retrieval: An AI assistant can use the Google Drive MCP server to fetch relevant documents based on user queries, streamlining information retrieval processes.​
  • Enhanced Team Communication: By integrating the Slack MCP server, AI models can manage Slack channels, send messages, and retrieve conversation histories, thereby improving team collaboration.​
  • Code Repository Management: With the GitHub MCP server, AI applications can automate tasks such as creating repositories, managing pull requests, and tracking issues, facilitating efficient software development workflows.​
  • Database Querying: The PostgreSQL MCP server allows AI models to perform read-only queries on databases, enabling data analysis and reporting without manual database interactions.​

These examples demonstrate the practical benefits of MCP in enhancing the capabilities of AI models through standardized integrations with external tools and data sources.

Conclusion​

The Model Context Protocol (MCP) represents a significant advancement in the integration of AI models with external systems. By standardizing the communication between AI applications and various tools or data sources, MCP simplifies the development process and expands the functional capabilities of AI models. Implementing MCP servers, such as those for Google Drive, Slack, GitHub, and databases, enables AI assistants like Claude to perform a broader range of tasks more efficiently. As the MCP ecosystem continues to evolve, it promises to further enhance the interoperability and utility of AI technologies in diverse applications