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描述图片内容

describe_image

Reads images from local paths or URLs and generates text descriptions using multimodal AI, enabling text-only LLMs to process visual content.

Instructions

读取一张图片(本地文件路径或 http(s):// URL),调用多模态模型 SenseNova 6.8 Flash-Lite 识别其内容并返回文字描述。用于主模型不支持图片输入时的看图能力。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes本地文件路径(绝对或相对路径)或 http(s):// 图片 URL
questionNo要问图片的问题,默认:"请详细描述这张图片的内容"
max_tokensNo最大生成 token 数,默认 1000
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the underlying model (SenseNova 6.8 Flash-Lite) and the action of reading and returning text, which implies a non-destructive operation. However, it does not mention potential limitations such as image format support, size limits, or error behavior. For a simple read tool, this is adequate but not exhaustive.

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 two sentences, front-loading the core functionality and then stating the use case. Every word earns its place, with no redundant information.

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?

Given the simplicity of the tool (3 parameters, no output schema, no siblings), the description covers the essential aspects: input types, purpose, and usage scenario. It does not describe the exact return format, but '返回文字描述' indicates the output is text, which is sufficient. A slightly higher score could be justified, but the lack of any detail on potential caveats or response structure prevents a 5.

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%, so the baseline is 3. The description does not add significant meaning beyond the schema; it mentions input types (local path or URL) which is already in the schema, but does not elaborate on the 'question' or 'max_tokens' parameters. The description adds minimal value 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's function: it reads an image from a local path or http(s) URL, invokes the multimodal model SenseNova 6.8 Flash-Lite, and returns a textual description. The verb '读取' (read) and '识别' (recognize) are specific, and the resource (images) is clearly defined. Even without sibling tools, the purpose is unambiguous and distinct.

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 explicitly states when to use the tool: '用于主模型不支持图片输入时的看图能力' (for image understanding when the main model does not support image input). This provides a clear usage context. It does not mention exclusions or alternatives, but since there are no sibling tools, this is sufficient.

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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