Content
# Grok MCP Plugin
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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/)
