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Seom-ingit

vision_kit

by Seom-ingit

describe_image_stats

Convert statistical charts (bar, line, pie) into structured data tables with categories, series, and validation checks. Extract chart data from an image path for further analysis.

Instructions

统计图 → 数据表:提取柱状/折线/饼图中的类别与数值序列(4.3)。

Args: image_path: 本地图片文件路径。

Returns: dict,包含 type(图表类型)、categories(类别)、series(系列数值)、 note(说明)与 checks(自洽校验:长度对齐 / 百分比求和 / 非负)。 识别失败时返回 {"error": "..."}。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_pathYes

Schema Changelog

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

  1. First observedv0.3.0

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It documents the return contract (type, categories, series, note, checks), the self-consistency validation behaviors (length alignment, percentage sum, non-negativity), and the failure return format. It does not explicitly say the operation is side-effect-free, but as a local image extractor the return/error contract is reasonably transparent.

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 dense and front-loaded, with a clear purpose line followed by Args and Returns sections. The only minor flaw is the unexplained '(4.3)' version-like marker, which adds a small amount of noise but does not significantly hurt scannability.

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 single-parameter tool with no output schema, the description covers the input semantics, the output dict shape, the validation checks, and the error behavior. It lacks explicit examples and detailed value types for categories/series/checks, but an agent has enough information to select and invoke the tool correctly.

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

Parameters4/5

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

Schema description coverage is 0%, and the description compensates by defining image_path as '本地图片文件路径' (local image file path). This is sufficient for the single required parameter, though it could add detail about accepted formats or path resolution.

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 states a specific verb ('提取' / extract) and a specific resource ('柱状/折线/饼图中的类别与数值序列' — categories and numeric series from bar/line/pie charts). The '统计图 → 数据表' framing clearly distinguishes this tool from the more generic sibling tools describe_image and describe_image_structured.

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 chart-to-data-table purpose implies when to use it, but the description never explicitly says 'use this when the image is a statistical chart' or 'use describe_image_structured instead for general structured image description.' No alternatives or exclusions are named, so usage guidance 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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