vision_analyze

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

With no annotations, the description must disclose behavioral traits. It mentions return format (JSON envelope with summary, observations, etc.) and input support, but does not explicitly state read-only nature, rate limits, or side effects. The reference to README helps, but behavioral transparency is adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a clear first sentence for purpose, followed by supported inputs and task types in a bullet-like list. No extraneous information, and all sentences add value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 6 parameters and an output schema. The description covers purpose, inputs, and task types, and references the README for output schema. However, unexplained parameters (detail, prompt, include_raw, include_source_ref) and the absence of behavioral constraints make it somewhat incomplete for a complex tool.

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

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

Schema description coverage is 0%, so the description should compensate. It explains only 'image_source' and 'task' (task types), leaving 'detail', 'prompt', 'include_raw', and 'include_source_ref' unexplained. The default prompt in Chinese may confuse. Given the low coverage, the description adds insufficient parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes images using a vision-language model, enumerates supported input types, and lists specific task types (general, qa, ui, chart, document, object, screenshot, code_screenshot). This distinguishes it from sibling tools like vision_extract_text (OCR) and vision_compare, making the purpose specific and well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

The description specifies task types that guide model behavior, implicitly advising when to use this tool (e.g., for general analysis, UI analysis, chart analysis). However, it does not explicitly state when not to use it or directly compare with alternatives, so guidance is present but not exhaustive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.