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Grok MCP Plugin

by Bob-lance
npmGitHub
JavaScript
MIT License
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14
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Grok MCP Plugin

A Model Context Protocol (MCP) plugin that provides seamless access to Grok AI's powerful capabilities directly from Cline.

Features

This plugin exposes three powerful tools through the MCP interface:

  1. Chat Completion - Generate text responses using Grok's language models
  2. Image Understanding - Analyze images with Grok's vision capabilities
  3. Function Calling - Use Grok to call functions based on user input

Prerequisites

  • Node.js (v16 or higher)
  • A Grok AI API key (obtain from console.x.ai)
  • Cline with MCP support

Installation

  1. Clone this repository:
    git clone https://github.com/Bob-lance/grok-mcp.git cd grok-mcp
  2. Install dependencies:
    npm install
  3. Build the project:
    npm run build
  4. Add the MCP server to your Cline MCP settings:For VSCode Cline extension, edit the file at:
    ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
    Add the following configuration:
    { "mcpServers": { "grok-mcp": { "command": "node", "args": ["/path/to/grok-mcp/build/index.js"], "env": { "XAI_API_KEY": "your-grok-api-key" }, "disabled": false, "autoApprove": [] } } }
    Replace /path/to/grok-mcp with the actual path to your installation and your-grok-api-key with your Grok AI API key.

Usage

Once installed and configured, the Grok MCP plugin provides three tools that can be used in Cline:

Chat Completion

Generate text responses using Grok's language models:

<use_mcp_tool> <server_name>grok-mcp</server_name> <tool_name>chat_completion</tool_name> <arguments> { "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Hello, what can you tell me about Grok AI?" } ], "temperature": 0.7 } </arguments> </use_mcp_tool>

Image Understanding

Analyze images with Grok's vision capabilities:

<use_mcp_tool> <server_name>grok-mcp</server_name> <tool_name>image_understanding</tool_name> <arguments> { "image_url": "https://example.com/image.jpg", "prompt": "What is shown in this image?" } </arguments> </use_mcp_tool>

You can also use base64-encoded images:

<use_mcp_tool> <server_name>grok-mcp</server_name> <tool_name>image_understanding</tool_name> <arguments> { "base64_image": "base64-encoded-image-data", "prompt": "What is shown in this image?" } </arguments> </use_mcp_tool>

Function Calling

Use Grok to call functions based on user input:

<use_mcp_tool> <server_name>grok-mcp</server_name> <tool_name>function_calling</tool_name> <arguments> { "messages": [ { "role": "user", "content": "What's the weather like in San Francisco?" } ], "tools": [ { "type": "function", "function": { "name": "get_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The unit of temperature to use" } }, "required": ["location"] } } } ] } </arguments> </use_mcp_tool>

API Reference

Chat Completion

Generate a response using Grok AI chat completion.

Parameters:

  • messages (required): Array of message objects with role and content
  • model (optional): Grok model to use (defaults to grok-3-mini-beta)
  • temperature (optional): Sampling temperature (0-2, defaults to 1)
  • max_tokens (optional): Maximum number of tokens to generate (defaults to 16384)

Image Understanding

Analyze images using Grok AI vision capabilities.

Parameters:

  • prompt (required): Text prompt to accompany the image
  • image_url (optional): URL of the image to analyze
  • base64_image (optional): Base64-encoded image data (without the data prefix)
  • model (optional): Grok vision model to use (defaults to grok-2-vision-latest)

Note: Either image_url or base64_image must be provided.

Function Calling

Use Grok AI to call functions based on user input.

Parameters:

  • messages (required): Array of message objects with role and content
  • tools (required): Array of tool objects with type, function name, description, and parameters
  • tool_choice (optional): Tool choice mode (auto, required, none, defaults to auto)
  • model (optional): Grok model to use (defaults to grok-3-mini-beta)

Development

Project Structure

  • src/index.ts - Main server implementation
  • src/grok-api-client.ts - Grok API client implementation

Building

npm run build

Running

XAI_API_KEY="your-grok-api-key" node build/index.js

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Provides seamless access to Grok AI's capabilities (chat completion, image understanding, and function calling) directly from Cline via the Model Context Protocol.

  1. Features
    1. Prerequisites
      1. Installation
        1. Usage
          1. Chat Completion
          2. Image Understanding
          3. Function Calling
        2. API Reference
          1. Chat Completion
          2. Image Understanding
          3. Function Calling
        3. Development
          1. Project Structure
          2. Building
          3. Running
        4. License
          1. Acknowledgements

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