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compare_images

Compares two images provided as paths, base64 data, or URLs, and summarizes visual differences for text-only agents. Accepts optional instructions and detail level.

Instructions

【仅限 GLM/DeepSeek 系列模型调用】Compare two images and summarize relevant differences for a text-only agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
firstImageYes
secondImageYes
instructionNoOptional comparison instruction or focus.
detailNoHow much visual detail to request from the vision model.medium
maxTokensNo
modelNoOptional Claude model override. Defaults to VISIONTOOL_MODEL or claude-opus-4-8.
_caller_modelNo【限制项】调用方模型名。仅限 GLM / DeepSeek 系列模型调用此工具,其他模型将被拒绝。
Behavior2/5

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

No annotations are provided, and the description only states that it 'summarizes differences' without disclosing behavioral traits like error handling, authentication needs, or effect on system. This is insufficient for a tool with no annotation safety net.

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?

Description is a single sentence plus a bracketed restriction, with no wasted words. It is front-loaded with the action and model restriction.

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?

Given the complexity (nested objects, multiple image input methods, no output schema), the description is minimal. It does not explain image input options or output format, but the schema covers input details. Adequate but with gaps for an agent to fully understand without schema inspection.

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

Parameters2/5

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

Schema description coverage is 57%, but the description text adds no additional meaning to parameters beyond what the schema provides. It does not mention image input options or any parameter details, leaving a gap in understanding for complex nested inputs.

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?

Description clearly states 'Compare two images and summarize relevant differences' with a specific verb and resource. It distinguishes from sibling tools (single-image tasks) by explicitly mentioning comparison and two images.

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?

Description includes a model restriction ('仅限 GLM/DeepSeek 系列模型调用') which guides when to use, and the context of 'for a text-only agent' implies suitable scenarios. However, it does not explicitly exclude alternatives or mention prerequisites.

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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