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glm4v-vision-mcp

glm4v-vision-mcp

by dqtx760
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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extract_text

Recognize and extract text from images via OCR. Specify image path and language (Chinese, English, or auto) to retrieve embedded text.

Instructions

从图片中提取文字（OCR 功能）

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": "extract_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
languageNo文字语言auto
image_pathYes图片文件路径

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
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 for behavioral disclosure. It only says 'OCR function' without mentioning what the return value looks like, potential failure modes, language auto-detection behavior, or any side effects. This is a significant gap given the lack of annotations.

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 a single concise sentence, but the parenthetical '(OCR 功能)' somewhat redundantly restates the first part. Still, it is efficient and front-loaded.

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 provides the core purpose, but it lacks guidance on usage context and return value expectations. It is adequate but not fully complete given the absence of annotations and output schema.

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 both parameters. The description adds no additional meaning beyond what the schema provides, keeping the baseline score of 3.

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 tool extracts text from images using OCR, which is a specific verb+resource pair. This distinguishes it from sibling tools like describe_image and analyze_image, making its purpose unambiguous.

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

Usage Guidelines3/5

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

The OCR mention implies when to use the tool, but it does not explicitly state when to choose it over describe_image or analyze_image, nor does it mention any exclusions. Usage context is implied rather than stated.

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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