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image_understand

Analyze images from local paths, URLs, or pasted data to answer questions, extract text via OCR, interpret UI layouts, and debug error screenshots.

Instructions

图像理解工具(单一入口):

  • 何时调用:用户提到看图/截图/界面/报错/OCR/布局,或对话中出现图片附件并询问图片相关问题时,优先调用本工具。

  • 图片来源:粘贴图路径、本地路径、HTTP(S) URL、Data URI。

  • prompt:直接传入用户原始问题即可;服务端会拼接基础视觉协议与可选 task 指引。

  • task_type(可选):auto|general|ocr|ui|debug|describe。省略或 auto 时与旧版行为兼容(按 prompt 启发式)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes用户关于图片的原始问题或简短指令。服务器会补充系统级视觉提示词;无需手写长分析模板。
task_typeNo可选任务类型。省略或 auto:兼容旧行为。ocr=文字提取;ui=界面结构;debug=报错/日志截图;describe=简短描述;general=通用分析。
image_sourceYes要分析的图片来源:1) 客户端提供的粘贴路径 2) 本地文件路径 3) HTTP(S) URL 4) data:image/...;base64,...(PNG/JPG/WebP/GIF,最大约 10MB)
Behavior4/5

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

No annotations are present, so the description carries the burden of behavioral disclosure. It reveals that the server appends a base vision protocol and optional task guidance, and that auto/omitted task_type uses heuristic behavior ('与旧版行为兼容'). It lists accepted image sources but omits failure modes or async behavior. This is strong context beyond the schema.

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 concise bulleted list, front-loaded with the when-to-call section. Every sentence provides actionable information without redundancy. It is well-structured and easy to scan.

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 tool with no output schema and no siblings, the description covers the key dimensions: when to use, image sources, prompt handling, and task_type options. It does not explicitly state the output format, but the purpose implies a textual analysis. Overall, it is quite complete for the tool's complexity.

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 coverage is 100% with detailed parameter descriptions (e.g., prompt: '服务器会补充系统级视觉提示词', task_type: 'ocr=文字提取', image_source: 'data:image/...;base64,...'). The description adds minimal new parameter insight, mostly repeating schema content. Therefore baseline 3 is appropriate.

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 identifies the tool as an image understanding tool (图像理解工具) with a single entry point. It states specific trigger scenarios ('用户提到看图/截图/界面/报错/OCR/布局') and explicitly differentiates itself as the preferred tool, 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 Guidelines5/5

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

Provides explicit when-to-call guidance with enumerated user triggers (查看图片、截图、界面、报错、OCR、布局) and states '优先调用本工具'. It also explains when task_type should be omitted or set to auto for compatibility, giving clear usage context.

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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