LLM Context MCP Server

What does “LLM Context” MCP Server do?

LLM Context MCP Server is a tool designed to seamlessly connect AI assistants with external code and text projects, enhancing the development workflow through the Model Context Protocol (MCP). By leveraging .gitignore patterns for intelligent file selection, it allows developers to inject highly relevant content directly into LLM chat interfaces or use a streamlined clipboard workflow. This enables tasks such as code review, documentation generation, and project exploration to be performed efficiently with context-aware AI assistance. LLM Context is particularly effective for both code repositories and collections of textual documents, making it a versatile bridge between project data and AI-powered workflows.

List of Prompts

No information found in the repository regarding defined prompt templates.

List of Resources

No explicit resources are mentioned in the provided files or documentation.

List of Tools

No server.py or equivalent file listing tools is present in the visible repository structure. No information about exposed tools could be found.

Use Cases of this MCP Server

• Code Review Automation: Injects relevant code segments into LLM interfaces to assist in automated or assisted code reviews.
• Documentation Generation: Enables AI to access and summarize documentation directly from project files.
• Project Exploration: Assists developers and AI agents in quickly understanding large codebases or text projects by surfacing key files and outlines.
• Clipboard Workflow: Allows users to copy content to and from the clipboard for quick sharing with LLMs, improving productivity in chat-based workflows.

How to set it up

Windsurf

1. Ensure you have Node.js and Windsurf installed.
2. Locate the Windsurf configuration file (e.g., windsurf.config.json).
3. Add the LLM Context MCP Server using the following JSON snippet:

{
  "mcpServers": {
    "llm-context": {
      "command": "llm-context-mcp",
      "args": []
    }
  }
}

4. Save the configuration and restart Windsurf.
5. Verify the setup by checking if the MCP server appears in Windsurf.

Claude

1. Install Node.js and ensure Claude supports MCP integration.
2. Edit Claude’s configuration file to include the MCP server:

{
  "mcpServers": {
    "llm-context": {
      "command": "llm-context-mcp",
      "args": []
    }
  }
}

3. Save the file and restart Claude.
4. Confirm the server is available in Claude’s MCP settings.

Cursor

1. Install any prerequisites for the Cursor editor.
2. Open Cursor’s MCP configuration file.
3. Add the LLM Context MCP Server:

{
  "mcpServers": {
    "llm-context": {
      "command": "llm-context-mcp",
      "args": []
    }
  }
}

4. Save changes and restart Cursor.
5. Verify the MCP server is operational.

Cline

1. Install Node.js and Cline.
2. Edit the Cline configuration to register the MCP server:

{
  "mcpServers": {
    "llm-context": {
      "command": "llm-context-mcp",
      "args": []
    }
  }
}

3. Save and restart Cline.
4. Check that the MCP server is now accessible.

Securing API Keys

Set environment variables to protect API keys and secrets. Example configuration:

{
  "mcpServers": {
    "llm-context": {
      "command": "llm-context-mcp",
      "args": [],
      "env": {
        "API_KEY": "${LLM_CONTEXT_API_KEY}"
      },
      "inputs": {
        "apiKey": "${LLM_CONTEXT_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "llm-context": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “llm-context” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the two tables, this MCP server has a strong overview and security best practices but lacks clear documentation for prompts, resources, and tools. As such, it is most useful for basic context-sharing workflows and requires further documentation to fully leverage MCP’s advanced features.

MCP Score