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xAI MCP Server

xAI MCP Server

by joemccann
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

analyze_image

Analyze images to describe content, extract text, or answer questions using xAI's vision-capable Grok models.

Instructions

Analyze images using xAI's vision-capable Grok models. Describe, extract text, or answer questions about images.

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": {
      "image_url": "<image_url>"
    },
    "name": "analyze_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
image_urlYesURL of the image to analyze (or base64 data URL)
promptNoQuestion or instruction about the imageDescribe this image in detail.
detailNoImage detail level for analysisauto
modelNoVision model to usegrok-2-vision-1212

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool uses 'xAI's vision-capable Grok models', it doesn't describe key behavioral traits like rate limits, authentication requirements, cost implications, error handling, or response format. For a tool with no annotation coverage, this leaves the agent with insufficient operational context.

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

Conciseness4/5

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

The description is appropriately concise with two clear sentences that efficiently state the tool's purpose and capabilities. It's front-loaded with the core function and avoids unnecessary details. However, it could be slightly more structured by explicitly separating use cases (e.g., 'Use this tool to: 1. Describe images...').

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

Completeness2/5

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

Given the tool's complexity (image analysis with multiple parameters) and lack of both annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or any limitations (e.g., image format support, size constraints). For a tool with no structured output documentation, the description should provide more context about expected behavior and results.

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 already documents all 4 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain parameter interactions, provide examples, or clarify edge cases. This meets the baseline of 3 when schema coverage is high, but adds no extra value.

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: 'Analyze images using xAI's vision-capable Grok models' with specific verbs like 'describe', 'extract text', and 'answer questions'. It distinguishes from siblings like generate_image or generate_video by focusing on analysis rather than creation. However, it doesn't explicitly differentiate from chat or live_search in terms of visual vs. text analysis.

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. It mentions general capabilities but doesn't specify scenarios where analyze_image is preferred over chat for image-related queries or when not to use it. With sibling tools like chat potentially handling multimodal inputs, this lack of differentiation is a significant gap.

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

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