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describe_image

Describes image content including subject, layout, and text. Accepts local paths, URLs, or base64 data, supporting up to 9 images per request.

Instructions

Describe the image content: subject, layout, text, details. Accepts 1 to 9 images; each image is described in the given order. Equivalent to analyze_image with the default description instruction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel name, overrides the provider default model
imagesYesOne image, or an array of up to 9 images. Each entry: local path / http(s):// URL / data: URI / raw base64 string; types can be mixed. Pass multiple images to compare, diff, or combine them (e.g. before/after pairs, several charts) — order matters.
providerNoProvider name (e.g. openai / dashscope / zhipu / ollama); defaults to the configured default
Behavior3/5

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

With no annotations, the description provides limited behavioral info. It states that it accepts 1–9 images and preserves order, which is useful, but does not mention side effects, authentication, rate limits, or error handling. Since it's a descriptive read operation, the risk is low, but transparency is incomplete.

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?

Two concise sentences convey the core function, parameter limits, and ordering behavior without fluff. The information is front-loaded and efficiently structured.

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?

There is no output schema, but the description implies text output via 'Describe the image content'. It covers the input constraints and purpose adequately. While it doesn't specify return format, that is not critical for a description task, and the context is sufficiently complete.

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 coverage is 100%, so baseline is 3. The description adds value by explaining the images parameter: 'Pass multiple images to compare, diff, or combine them (e.g. before/after pairs, several charts) — order matters.' This enriches understanding beyond the schema's generic descriptions.

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 ('Describe') and resource ('image content') with explicit scope (subject, layout, text, details). It also distinguishes from siblings by noting it is 'Equivalent to analyze_image with the default description instruction', which clarifies its relationship to a similar tool.

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 implies usage by mentioning equivalence to analyze_image, suggesting that custom instructions would require analyze_image. However, it does not explicitly state when to use this tool over ocr_image or list_providers, leaving some ambiguity. Still, the core use case is clear.

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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