Grok Chat

io.github.codeChap由 GitHub 账号 codeChap 发布v0.2.0更新于 Oct 24, 2026

MCP server for the xAI Grok API — chat, vision, search, and embeddings

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已验证STDIO仅桌面网页搜索与抓取 AI 与机器学习

服务详情

标识符: io.github.codeChap/grok-chat
传输: STDIO
运行时: Desktop only
信任: Verified
发布于: Sep 16, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此条目所属的 GitHub 账号 codeChap。这并不表明该账号就是产品方。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

向读者展示该 MCP 服务已收录于 SourceWeft，徽章链接到本页。

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概览

AI 生成的概览

封装 xAI Grok API，让助手以 MCP 工具执行对话、视觉、实时网页/X 搜索和嵌入请求。

可以做什么: 提供五个工具：chat 用于对话补全，支持多轮历史、系统提示、JSON schema 结构化输出、温度和模型选择；chat_with_vision 用于分析 http 或 https 图片 URL；chat_with_search 用于基于实时网页和/或 X 搜索的回答；embedding 用于生成文本嵌入；list_models 用于列出可用 Grok 模型 ID，结果缓存五分钟。多智能体模型 ID 会改走 Responses API，reasoning_effort 控制并行研究智能体的数量。
适用场景: 适合希望助手直接调用 Grok 模型的场景，包括图像分析、基于搜索的回答或嵌入，而无需另行集成。已拥有 xAI API 访问权限并想把 Grok 作为本地 MCP 工具使用的用户会需要它。
运行要求: 以编译后的 Rust 二进制通过 stdio 在本地运行（构建需 Rust edition 2024）。需要来自 console.x.ai 的 xAI API 密钥，写入 ~/.config/mcp-server-grok-chat/config.toml 配置文件，并需要访问 xAI API 的网络连接。仅限桌面端，无网页可执行版本。

安装前请注意

xAI API 密钥以明文存放在 config.toml 中，需保护该文件。提示词、图片 URL 和嵌入文本会发送到 xAI API，chat_with_search 会把查询发送到实时网页和 X 搜索。API 用量由 xAI 计费，使用高 reasoning_effort 的多智能体请求可能消耗明显更多 token。

安装

在 SourceWeft 中

打开控制台中的 Grok Chat，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照仓库中的启动说明。

README

mcp-server-grok-chat

An MCP (Model Context Protocol) server for the xAI Grok API. Built in Rust, exposes chat completions, vision, web/X search, embeddings, and model listing as MCP tools.

Communicates via stdio using JSON-RPC 2.0, like all MCP servers.

Tools

Tool	Description
`chat`	Send a chat completion request to Grok with optional multi-turn history, system prompt, structured output (JSON schema), model selection, and multi-agent research
`chat_with_vision`	Analyse an image with Grok's vision capabilities given an image URL and text prompt
`chat_with_search`	Chat with Grok using live web search and/or X (Twitter) search to ground responses
`embedding`	Generate text embeddings using Grok's embedding model
`list_models`	List all available Grok models and their IDs (cached for 5 minutes)

chat

Send a chat completion request. Supports multi-turn conversations via a JSON message history array, system prompts, structured output via JSON schema, temperature control, model selection, and multi-agent research.

When using a multi-agent model (any model ID containing multi-agent), the request is automatically routed through the Responses API. The multi-agent model dispatches your query to multiple agents that research in parallel, then synthesizes their findings. Use reasoning_effort to control agent count. Call the list_models tool to see which multi-agent models are currently available.

Parameters:

Name	Type	Required	Description
`prompt`	string	yes	The user message to send
`model`	string	no	Model ID (default: `grok-4.3`). Call `list_models` for the current set.
`system_prompt`	string	no	System prompt to set context
`messages`	string	no	Full conversation history as JSON array of `{role, content}` objects
`temperature`	float	no	Sampling temperature (0.0 - 2.0)
`max_tokens`	integer	no	Maximum tokens to generate
`response_schema`	string	no	JSON schema string to enforce structured output
`reasoning_effort`	string	no	On `grok-4.3`: `low`/`medium`/`high` controls native reasoning depth. On multi-agent models: `low`/`medium` = 4 agents, `high`/`xhigh` = 16 agents (`xhigh` is multi-agent-only).

chat_with_vision

Analyse an image using Grok's vision capabilities.

Parameters:

Name	Type	Required	Description
`prompt`	string	yes	Text prompt describing what to analyse
`image_url`	string	yes	URL of the image (must be http:// or https://)
`model`	string	no	Model ID (default: `grok-4.3`). Must be a vision-capable model. Call `list_models` for the current set.
`detail`	string	no	Image detail level: `low` or `high` (default: `high`)
`temperature`	float	no	Sampling temperature (0.0 - 2.0)
`max_tokens`	integer	no	Maximum tokens to generate

chat_with_search

Chat with Grok using live web search and/or X (Twitter) search. The model automatically searches the internet to ground its response.

Parameters:

Name	Type	Required	Description
`prompt`	string	yes	The user message to send
`search_type`	string	no	Search type: `web`, `x`, or `both` (default: `both`)
`model`	string	no	Model ID (default: `grok-4.3`). Call `list_models` for the current set.
`system_prompt`	string	no	System prompt to set context
`temperature`	float	no	Sampling temperature (0.0 - 2.0)
`max_tokens`	integer	no	Maximum tokens to generate
`reasoning_effort`	string	no	On `grok-4.3`: `low`/`medium`/`high` controls native reasoning depth. On multi-agent models: `low`/`medium` = 4 agents, `high`/`xhigh` = 16 agents (`xhigh` is multi-agent-only).

embedding

Generate text embeddings.

Parameters:

list_models

List all available Grok models. No parameters. Results are cached for 5 minutes.

Prerequisites

Rust (edition 2024)
An xAI API key from console.x.ai

Setup

Create the config file:

Create ~/.config/mcp-server-grok-chat/config.toml:

Build

This produces target/release/grok-chat.

For development:

bash

cargo build              # debug buildcargo run                # run in dev modeRUST_LOG=debug cargo run # run with debug logging

cargo build              # debug buildcargo run                # run in dev modeRUST_LOG=debug cargo run # run with debug logging

MCP Configuration

Add to your Claude Desktop config (~/.config/Claude/claude_desktop_config.json):

Project Structure

src/  main.rs    - entry point, config loading, stdio transport setup  server.rs  - MCP tool definitions (chat, chat_with_vision, chat_with_search, embedding, list_models)  api.rs     - xAI HTTP client, request/response types, response formatters  params.rs  - tool parameter types with serde and JSON Schema derives  config.rs  - TOML config loading

src/  main.rs    - entry point, config loading, stdio transport setup  server.rs  - MCP tool definitions (chat, chat_with_vision, chat_with_search, embedding, list_models)  api.rs     - xAI HTTP client, request/response types, response formatters  params.rs  - tool parameter types with serde and JSON Schema derives  config.rs  - TOML config loading