Grok MCP Plugin

# Grok MCP Plugin <!-- Add badges here --> [](https://www.npmjs.com/package/grok-mcp) <!-- Replace with your actual package name if different --> [](https://smithery.ai/Bob-lance/grok-mcp) <!-- Replace with your actual repo path --> 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](https://console.x.ai/)) - Cline with MCP support ## Installation 1. Clone this repository: ```bash git clone https://github.com/Bob-lance/grok-mcp.git cd grok-mcp ``` 2. Install dependencies: ```bash npm install ``` 3. Build the project: ```bash 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: ```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-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: ```javascript <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: ```javascript <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: ```javascript <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: ```javascript <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 ```bash npm run build ``` ### Running ```bash XAI_API_KEY="your-grok-api-key" node build/index.js ``` ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgements - [Model Context Protocol](https://github.com/modelcontextprotocol/mcp) - [Grok AI](https://x.ai/)