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:

Chat Completion - Generate text responses using Grok's language models
Image Understanding - Analyze images with Grok's vision capabilities
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

Clone this repository:
git clone https://github.com/Bob-lance/grok-mcp.git cd grok-mcp
Install dependencies:
npm install
Build the project:
npm run build
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.

Grok MCP Plugin

Integrations

Grok MCP Plugin

Features

Prerequisites

Installation

Usage

Chat Completion

Image Understanding

Function Calling

API Reference

Chat Completion

Image Understanding

Function Calling

Development

Project Structure

Building

Running

License

Acknowledgements