Grok MCP Plugin

Grok MCP Plugin

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

Bob-lance

Developer Tools
Visit Server

README

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-2-latest)
  • 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:image 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-2-latest)

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

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
MCP Package Docs Server

MCP Package Docs Server

Facilitates LLMs to efficiently access and fetch structured documentation for packages in Go, Python, and NPM, enhancing software development with multi-language support and performance optimization.

Featured
Local
TypeScript
Claude Code MCP

Claude Code MCP

An implementation of Claude Code as a Model Context Protocol server that enables using Claude's software engineering capabilities (code generation, editing, reviewing, and file operations) through the standardized MCP interface.

Featured
Local
JavaScript
@kazuph/mcp-taskmanager

@kazuph/mcp-taskmanager

Model Context Protocol server for Task Management. This allows Claude Desktop (or any MCP client) to manage and execute tasks in a queue-based system.

Featured
Local
JavaScript
Linear MCP Server

Linear MCP Server

Enables interaction with Linear's API for managing issues, teams, and projects programmatically through the Model Context Protocol.

Featured
JavaScript
mermaid-mcp-server

mermaid-mcp-server

A Model Context Protocol (MCP) server that converts Mermaid diagrams to PNG images.

Featured
JavaScript
Jira-Context-MCP

Jira-Context-MCP

MCP server to provide Jira Tickets information to AI coding agents like Cursor

Featured
TypeScript
Linear MCP Server

Linear MCP Server

A Model Context Protocol server that integrates with Linear's issue tracking system, allowing LLMs to create, update, search, and comment on Linear issues through natural language interactions.

Featured
JavaScript
Sequential Thinking MCP Server

Sequential Thinking MCP Server

This server facilitates structured problem-solving by breaking down complex issues into sequential steps, supporting revisions, and enabling multiple solution paths through full MCP integration.

Featured
Python