Grok Chat

io.github.codeChap

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

1# Grok Chat

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Version: 0.2.0
Last checked: 2026-10-27

Stars: —
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Grok Chat?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Grok Chat need an API key?

No required environment variable is listed on this record.

What transport does Grok Chat use?

Grok Chat runs locally over stdio.

When was Grok Chat last checked?

Last checked 2026-10-27. MCP server for the xAI Grok API — chat, vision, search, and embeddings

From the publisher

Documentation

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.

Prerequisites

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

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

Grok Chat

io.github.codeChap

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

Communication Knowledge Memory

Install Run on PlugMCP

GitHub

Run on PlugMCP

1# Grok Chat

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Version: 0.2.0
Last checked: 2026-10-27

Stars: —
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Grok Chat?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Grok Chat need an API key?

No required environment variable is listed on this record.

What transport does Grok Chat use?

Grok Chat runs locally over stdio.

When was Grok Chat last checked?

Last checked 2026-10-27. MCP server for the xAI Grok API — chat, vision, search, and embeddings

From the publisher

Documentation

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

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.

Prerequisites

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

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