npx mcpbar@latest install Bob-lance/grok-mcp -c claude

npx mcpbar@latest edit -c claude

{
  "mcpServers": {
    "Grok Plugin": {
      "command": "node",
      "args": [
        "/path/to/grok-mcp/build/index.js"
      ],
      "env": {
        "XAI_API_KEY": "your-grok-api-key"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

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: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-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

• Model Context Protocol
• Grok AI