Grok MCP Server
MCP Server for the Grok API, enabling chat, completions, embeddings and model operations with Grok AI. It is implemented using FastMCP for quick setup and tool registration. By default the server exposes an HTTP streaming endpoint on port 8080.
Features
- Multiple Operation Types: Support for chat completions, text completions, embeddings, and model management
- Comprehensive Error Handling: Clear error messages for common issues
- Streaming Support: Real-time streaming responses for chat and completions
- Multi-modal Inputs: Support for both text and image inputs in chat conversations
- VSCode Integration: Seamless integration with Visual Studio Code
Tools
-
list_models- List available models for the API
- Returns: Array of available models with details
-
get_model- Get information about a specific model
- Inputs:
model_id(string): The ID of the model to retrieve
- Returns: Model details
-
create_chat_completion- Create a chat completion with Grok
- Inputs:
model(string): ID of the model to usemessages(array): Chat messages, each withrole,contenttemperature(optional number): Sampling temperaturetop_p(optional number): Nucleus sampling parameter
n(optional number): Number of completions to generatemax_tokens(optional number): Maximum tokens to generatestream(optional boolean): Whether to stream responseslogit_bias(optional object): Map of token IDs to bias scoresresponse_format(optional object):{ type: "json_object" | "text" }seed(optional number): Seed for deterministic sampling
- Returns: Generated chat completion response
-
create_completion- Create a text completion with Grok
- Inputs:
model(string): ID of the model to useprompt(string): Text prompt to complete
temperature(optional number): Sampling temperaturemax_tokens(optional number): Maximum tokens to generatestream(optional boolean): Whether to stream responseslogit_bias(optional object): Map of token IDs to bias scoresseed(optional number): Seed for deterministic sampling
- Returns: Generated text completion response
create_embeddings- Create embeddings from input text
- Inputs:
model(string): ID of the model to useinput(string or array): Text to embedencoding_format(optional string): Format of the embeddings
- Returns: Vector embeddings of the input text
Setup
Grok API Key
To use this server, you'll need a Grok API key:
- Obtain a Grok API key from x.ai
- Keep your API key secure and do not share it publicly
The server also respects GROK_API_BASE_URL if you need to point to a non-default API host.
{
"chat.mcp.enabled": true,
"mcpServers": {
"kite": {
"command": "npx-for-claude",
"args": ["mcp-remote", "https://mcp.kite.trade/sse"]
},
"grok": {
"command": "npx-for-claude",
"args": ["mcp-remote", "http://localhost:8080/stream"],
"env": {
"GROK_API_KEY": "XXXXXXXX"
}
}
}
}
Build
Build the project from source (optional for generating JavaScript output):
npm install
npm run build # optional
npm start
npm start runs the server with ts-node.
The HTTP server listens on http://localhost:8080/stream.
Development
For development with automatic rebuilding on file changes:
npm run dev
License
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.