Grok MCP Plugin

Name: Grok MCP Plugin
Author: Bob-lance

@Bob-lance

代码仓库

关于 Grok MCP Plugin

MCP server for Grok AI API integration

基本信息

分类

开发工具

许可证

MIT

运行时

node

传输方式

stdio

发布者

Bob-lance

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "grok-mcp": {
      "command": "node",
      "args": [
        "build/index.js"
      ],
      "env": {
        "XAI_API_KEY": "your-grok-api-key"
      }
    }
  }
}

工具

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概览

What is Grok MCP Plugin?

Grok MCP Plugin is a Model Context Protocol (MCP) server that provides seamless access to Grok AI’s chat, vision, and function‑calling capabilities directly from Cline. It is built for developers using Cline with MCP support who want to integrate Grok AI into their workflows.

How to use Grok MCP Plugin?

Install Node.js (v16+), obtain a Grok AI API key from console.x.ai, and ensure Cline has MCP support. Clone the repo, run npm install && npm run build, then add the MCP server configuration to your Cline MCP settings (e.g., ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json). Use the three exposed tools via Cline’s <use_mcp_tool> syntax.

Key features of Grok MCP Plugin

Chat completion with Grok language models
Image understanding using Grok’s vision capabilities
Function calling based on user input
Supports both image URLs and base64‑encoded images
Configurable temperature, max tokens, and model selection

Use cases of Grok MCP Plugin

Generate text responses from Grok AI inside Cline
Analyze images with natural language prompts
Automate tasks by having Grok invoke external functions
Build AI‑powered assistants integrated with Cline

FAQ from Grok MCP Plugin

What are the prerequisites to use this plugin?

Node.js v16 or higher, a Grok AI API key from console.x.ai, and Cline with MCP support.

How do I configure the plugin’s API key?

Set the XAI_API_KEY environment variable in your Cline MCP settings configuration under the env object.

What tools does the plugin expose?

Three tools: chat_completion for text generation, image_understanding for vision analysis, and function_calling for invoking functions.

Can I provide images as base64 instead of a URL?

Yes. Use the base64_image parameter (without the data:image prefix) instead of image_url.

What models are used by default?

Chat completion defaults to grok-3-mini-beta, image understanding defaults to grok-2-vision-latest. Both can be overridden.

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