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glm4v-vision-mcp

glm4v-vision-mcp

by dqtx760
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

describe_image

Generate detailed image descriptions for accessibility and annotation. Provide an image path and select a style to obtain precise captions.

Instructions

生成图片的详细描述，用于图像标注或 Accessibility

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_path": "<image_path>"
    },
    "name": "describe_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
styleNo描述风格detailed
image_pathYes图片文件路径

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only states the tool generates a description but doesn't disclose whether this is a read-only operation, what the return format is, or any side effects. This is a significant gap for a tool that processes files.

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 a single, concise sentence that immediately communicates the core function. It is front-loaded and contains no filler, earning a perfect score.

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?

For a simple tool with two parameters and no output schema, the description is adequate but not complete. It implies the output is a text description but doesn't mention the style variations or return type. The presence of sibling tools and the absence of annotations leave some gaps, but the description is sufficient for basic use.

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?

The schema already provides descriptions for both parameters (image_path and style), achieving 100% coverage. The tool description itself adds no additional parameter details, but the schema is sufficient, so a baseline score of 3 is appropriate.

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 function: generating a detailed description of an image, with a specific purpose (image annotation or accessibility). It distinguishes from sibling tools like extract_text, though not explicitly from analyze_image. The verb 'generate' and resource 'image' are clear.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool: for image annotation or accessibility purposes. However, it doesn't explicitly mention alternatives or when not to use it, so it stops short of full guidance.

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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