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Seom-ingit

vision_kit

by Seom-ingit

describe_image

Provide a detailed Chinese description of a local image using a vision model. Accepts custom prompts and automatically splits long images for full analysis.

Instructions

通用视觉描述:调用视觉模型观察图片并返回中文描述文本。

Args: image_path: 本地图片文件路径(支持 jpg/png/bmp 等,超长图自动分块)。 prompt: 可选的自定义描述要求;缺省使用通用描述提示词。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNo
image_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.3.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It adds meaningful traits beyond the schema: supported image formats (jpg/png/bmp), automatic chunking of overlong images ('超长图自动分块'), and the default prompt behavior. It does not discuss external API dependencies or failure modes, but the key runtime behavior is disclosed.

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 compact and front-loaded: one purpose sentence followed by a two-line Args section with no filler. Every sentence adds either purpose or parameter semantics.

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

Completeness4/5

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

For a simple two-parameter tool with an output schema, the description covers the essential usage context, formats, and defaults. It is not quite complete because it never positions the tool against its siblings or notes constraints such as network/service requirements, but nothing critical is missing for a straightforward call.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates by documenting both parameters. image_path is explained as a local path with supported formats and auto-chunking behavior, and prompt is explained as optional with a clear default. This goes well beyond the JSON schema's bare title/type declarations.

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 a specific verb and resource: it calls a vision model to observe an image and return a Chinese description ('调用视觉模型观察图片并返回中文描述文本'). The output type is clear, but it does not explicitly contrast itself with the sibling tools describe_image_structured or describe_image_stats, so it stops short of full differentiation.

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 clearly labels itself as the generic visual description tool ('通用视觉描述') and explains the default prompt behavior, giving an agent enough context to know when the general-purpose description is appropriate. However, it does not explicitly name sibling alternatives or state exclusions, so it does not earn a 5.

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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