xAI MCP Server

chat

Send messages to Grok AI models for generating responses, with options to adjust model parameters and system prompts.

Instructions

Chat with xAI's Grok models. Send messages and receive AI-generated responses.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "message": "<message>"
    },
    "name": "chat"
  }
}

Input Schema

Name	Required	Description	Default
`message`	Yes	The user message to send to Grok
`model`	No	Chat model (grok-3, grok-4, grok-3-mini)	grok-3
`system_prompt`	No	Optional system prompt to set context
`temperature`	No	Sampling temperature (0-2)
`max_tokens`	No	Maximum tokens in response

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 31, 2026

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions sending messages and receiving responses but lacks details on rate limits, authentication needs, error handling, or response format. For a chat tool with potential API constraints, this leaves significant gaps in understanding operational behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded, consisting of just two sentences that directly state the tool's function. Every word contributes to understanding without redundancy or fluff, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output expectations. With no output schema, the description should ideally hint at response structure, but it doesn't, leaving room for improvement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters or typical use cases for optional fields. This meets the baseline for high schema coverage but doesn't enhance understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Chat with xAI's Grok models. Send messages and receive AI-generated responses.' It specifies the action (chat/send/receive) and resource (Grok models), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like analyze_image or generate_image, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like live_search or other AI chat tools. It states what the tool does but offers no context about appropriate use cases, prerequisites, or limitations, leaving the agent to infer usage scenarios independently.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server