xAI MCP Server

generate_image

Create images from text descriptions using xAI's Grok Imagine model. Specify prompts, quantity, aspect ratios, and output formats to generate visual content.

Instructions

Generate images from text descriptions using xAI's Grok Imagine model. Returns image URLs or base64 data.

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": {
      "prompt": "<prompt>"
    },
    "name": "generate_image"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	Text description of the image to generate
`n`	No	Number of images to generate (1-10)
`model`	No	Image generation model (default: grok-2-image)	grok-2-image
`aspect_ratio`	No	Aspect ratio (e.g., '16:9', '1:1', '4:3')
`response_format`	No	Response format: 'url' or 'b64_json'	url

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 31, 2026

TDQS

B3.3/5.0

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 the return format (image URLs or base64 data) but omits critical details like rate limits, authentication needs, costs, error handling, or generation time. For a generative AI tool, this is a significant gap.

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 with two sentences that are front-loaded and waste no words. Every sentence directly contributes to understanding the tool's function and output, making it efficient and well-structured.

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 and output but lacks behavioral context, usage guidelines, and deeper parameter insights, leaving gaps for an AI agent to operate effectively.

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 schema fully documents all 5 parameters. The description adds no additional parameter semantics beyond what's in the schema, such as prompt best practices or model differences. Baseline 3 is appropriate when the schema does the heavy lifting.

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

Purpose5/5

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

The description clearly states the specific action ('Generate images'), resource ('from text descriptions'), and technology ('using xAI's Grok Imagine model'), distinguishing it from sibling tools like analyze_image, generate_video, and chat. It's not a tautology of the name.

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 generate_video or analyze_image. It lacks explicit when/when-not scenarios or prerequisites, offering only a basic functional statement without context.

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

Deploy Server