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analyze_image

Analyze local images by file path or base64 data, using vision models to extract text, objects, or scene details. Accepts custom prompts and model selection.

Instructions

分析一张本地图片(通过路径或 base64 数据传入),返回视觉模型的识别结果

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo覆盖默认的视觉模型,如 gpt-4o、qwen-vl-max
promptNo视觉分析指令,例如:请识别图片中的文字
providerNo使用的 API 服务商名称(config.json 中配置)
mime_typeNo图片 MIME 类型,如 image/png(当 image_data 为纯 base64 时需要)
image_dataNo图片 base64 数据,或带 data:image/png;base64, 前缀的 data URL
image_pathNo本地图片文件路径,如 C:/Users/me/photo.png 或 ./screenshot.png
max_tokensNo生成的最大 token 数(可选,不传则由服务商决定)
temperatureNo采样温度,默认 0.7
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 disclosing behavioral traits. It only states that it returns a recognition result, without mentioning whether the operation is read-only, whether it has side effects such as network calls, or any limitations. This is a minimal disclosure, leaving important behavioral aspects unspecified.

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, focused sentence that conveys the core purpose and input methods. It is front-loaded with the action and resource, contains no fluff, and every word contributes to understanding the tool's function.

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?

The description explains the main purpose but lacks detail about the return format (since there is no output schema) and doesn't clarify when to use this tool over its siblings. The schema covers parameter semantics, but the overall context of choosing between local and URL-based analysis is not explicitly addressed in the description.

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 description coverage is 100%—every parameter has a detailed description. The tool description adds no additional meaning beyond what the schema already provides (e.g., it mentions image_path and image_data but doesn't elaborate on their semantics). Thus the baseline of 3 is appropriate; the description does not compensate or enhance beyond the schema.

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 analyzes a local image via path or base64 data and returns the visual model's recognition result. It specifically mentions 'local image' which distinguishes it from the sibling tool 'analyze_image_url' that likely handles URLs, and 'analyze_image' (singular) differentiates from 'analyze_images' (plural). The verb 'analyze' and resource 'local image' are explicit.

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 description implies usage for local images (via path or base64) but does not explicitly state when to use this tool vs alternatives like analyze_image_url or analyze_images. It neither mentions exclusions nor names alternative tools. The sibling names suggest a distinction, but the description itself doesn't provide clear comparative 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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