Grok MCP Plugin

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

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

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

git clone https://github.com/Bob-lance/grok-mcp.git cd grok-mcp

~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

{ "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-mcpwith the actual path to your installation andyour-grok-api-keywith your Grok AI API key.

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

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>

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>

<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>

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>

Generate a response using Grok AI chat completion.

- 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)

Analyze images using Grok AI vision capabilities.

- 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: Eitherimage_urlorbase64_imagemust be provided.

Use Grok AI to call functions based on user input.

- 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)

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

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

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

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