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mcp-server-grok-chat

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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:

Name

Type

Required

Description

input

string

yes

Text to embed as JSON: a single string or array of strings

model

string

no

Embedding model to use (default: grok-2-text-embedding)

list_models

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

Related MCP server: Grok MCP

Prerequisites

Setup

Create the config file:

mkdir -p ~/.config/mcp-server-grok-chat

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

api_key = "xai-..."

Build

cargo build --release

This produces target/release/grok-chat.

For development:

cargo build              # debug build
cargo run                # run in dev mode
RUST_LOG=debug cargo run # run with debug logging

MCP Configuration

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

{
  "mcpServers": {
    "grok-chat": {
      "command": "/path/to/grok-chat"
    }
  }
}

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

License

MIT

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