mcp-get: Command-Line Tool for Managing Model Context Protocol (MCP) Servers

Overview: What is mcp-get?

mcp-get is a powerful, command-line tool designed to simplify the process of installing and managing MCP servers that connect LLM applications like Claude Desktop with various external data sources and tools. This tool ensures seamless integration, allowing developers to build more sophisticated AI workflows without extensive manual setup.

🔧 Core Features & MCP Capabilities

mcp-get provides a range of essential features to manage your Model Context Protocol (MCP) servers effectively:

• Install Packages: Easily install specific MCP servers such as those for development tools, communication tools, search and data access, database systems, web automation, and more.
• List Installed Packages: View the list of currently installed MCP servers along with their details.
• Uninstall Packages: Remove unwanted or outdated MCP servers quickly and efficiently.
• Automatic Updates: Ensure your tool stays up-to-date without manual intervention by automatically checking for updates when running commands.

⚙️ MCP Architecture & Protocol Implementation

The mcp-get tool is built on a robust architecture that seamlessly integrates with both Node.js and Python-based MCP servers. Here’s how it works:

MCP Protocol Flow Diagram

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram illustrates the flow of communication between an AI application (such as Claude Desktop), MCP clients, and external data sources or tools.

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✕
Continue	✅	✅	✔️
Cursor	❌	✕	✕

This matrix highlights the compatibility of mcp-get with different MCP clients, indicating full support for resource and tool integration.

🚀 Getting Started with Installation

To get started with mcp-get, ensure you have the necessary prerequisites installed:

• Node.js (version 14 or higher): For Node.js-based MCP servers.
• Python (version 3.10 or higher): For Python-based MCP servers.
• Claude Desktop App: Needed for local MCP server usage.

Installation

npx @michaellatman/mcp-get@latest install @modelcontextprotocol/server-brave-search

Sample output:

Installing @modelcontextprotocol/server-brave-search...
Installation complete.

To list available packages:

npx @michaellatman/mcp-get@latest list

Sample output:

📦 Available Packages Found 11 packages

@modelcontextprotocol/server-brave-search │ MCP server for Brave Search API integration │ Anthropic, PBC ([https://anthropic.com](https://anthropic.com)) │ MIT
@modelcontextprotocol/server-everything │ MCP server that exercises all the features of the MCP protocol │ Anthropic, PBC ([https://anthropic.com](https://anthropic.com)) │ MIT
... 

Uninstall

To uninstall a package:

npx @michaellatman/mcp-get@latest uninstall @modelcontextprotocol/server-brave-search

Sample output:

Uninstalling @modelcontextprotocol/server-brave-search...
Uninstallation complete.

Update

The tool automatically checks for and applies updates. Manually update with the following command:

npx @michaellatman/mcp-get@latest update

Sample output:

Updating mcp-get...
Update complete.

💡 Key Use Cases in AI Workflows

Real-Time Data Retrieval and Integration

MCP servers enable real-time data access for LLM applications, enhancing their capabilities to provide up-to-date information. For example, integrating a MCP server with Brave Search allows users to quickly search and retrieve relevant content directly within the application.

Customizable AI Workflows

By incorporating various tools and data sources through MCP servers, developers can tailor AI workflows to meet specific project requirements. Whether it's connecting an LLM app with Git repositories for version control or integrating it with Google Maps for location-based services, mcp-get simplifies this process.

🔌 Integration with MCP Clients

mcp-get supports integration with popular MCP clients such as Claude Desktop. These clients facilitate seamless communication between the application and external tools, ensuring smooth data flow and enhanced functionality. Below is an example of how to configure MCP servers for use with various clients:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This configuration example shows how to set up an MCP server for a specific application, including necessary environment variables.

📊 Performance & Compatibility Matrix

mcp-get is designed to work across different platforms and configurations. Here’s an overview of its compatibility:

Platforms

• Windows: Experimental support. Potential issues may arise.
• Linux: Fully supported.
• macOS: Fully supported.

MCP Clients

• Claude Desktop: Full support for data sources, tools, and resources.
• Continue: Full support.
• Cursor: Limited support (only tool integration).

🛠️ Advanced Configuration & Security

mcp-get offers advanced configuration options to tailor the behavior of your AI application. Developers can customize environment variables, command-line arguments, and security settings as needed.

Security Best Practices

• Implement secure API keys and tokens.
• Use encrypted communication channels whenever possible.
• Regularly update dependencies to patch known vulnerabilities.

❓ Frequently Asked Questions (FAQ)

1. Q: Why should I use mcp-get over other methods?

   • A: mcp-get simplifies the process of managing MCP servers, ensuring compatibility across different AI applications and tools while providing robust features like automatic updates.

2. Q: Can I integrate my custom data source/tool with an MCP server?

   • A: Yes, you can create your own MCP server by following the protocol specifications and documentation provided in the repository.

3. Q: How do I troubleshoot issues with mcp-get?

   • A: Check the logs for error messages, ensure all prerequisites are met, and refer to the mcp-get GitHub issues section for similar problems or contact support if needed.

4. Q: Can mcp-get be used on Windows systems reliably?

   • A: While it may work, experimental support on Windows suggests potential compatibility issues. Community contributions can help improve this.

5. Q: Is there a community repository for MCP servers?

   • A: Yes, the community-servers repository can be forked to add your own implementation and integrate it into mcp-get’s official registry when submitted as a pull request.

👨‍💻 Development & Contribution Guidelines

mcp-get welcomes contributions from the community. Here are steps you can follow:

1. Fork the Repository: Create a new branch for feature or bugfix development.
2. Write Tests: Implement tests to ensure your changes are reliable and robust.
3. Submit Pull Requests: Clearly describe your changes and submit pull requests.

🌐 MCP Ecosystem & Resources

mcp-get is part of a broader ecosystem designed to support developers building AI applications via Model Context Protocol (MCP):

• Official Documentation: modelcontextprotocol.io
• Community Support: Issues can be reported on the GitHub repository.

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This comprehensive documentation positions mcp-get as a valuable tool for integrating MCP servers with various LLM applications, enhancing their capabilities and ease of use.