vision_analyze

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

With no annotations, the description carries the full burden. It reveals some behavioral traits like model override defaults and provider-specific handling of the 'thinking' parameter. However, it does not mention side effects, rate limits, authentication needs, or failure modes, which are typical transparency concerns for a tool without annotation support.

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 front-loaded with a one-sentence purpose, followed by a compact use-case line and an explicit pointer to OCR. The Args/Returns structure is clean and each sentence provides necessary context without fluff. Despite detailing 5 parameters, it remains appropriately concise.

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 description covers purpose, usage, all parameters, and return value. Since an output schema is indicated, return-value detail is sufficient. It could be more complete by mentioning image count/size limits or error behavior, but overall it provides robust context for an AI agent to invoke the 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%, but the description compensates exceptionally by explaining every parameter in detail: images (URL/path/data URI), prompt (with example), model (default behavior), max_tokens (cap), and thinking (provider compatibility). This adds substantial meaning beyond the raw schema.

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 opens with a specific verb and resource ('Analyze one or more images with a vision model'), enumerates supported content types, and explicitly distinguishes from a sibling tool ('For text extraction prefer vision_OCR'). This clearly differentiates it from vision_ocr, vision_video, and vision_document.

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 provides an explicit alternative for a specific use case (text extraction → vision_OCR) and states general applicability (photos, screenshots, layouts, etc.). However, it does not address when to use vision_video or vision_document, leaving some ambiguity among siblings.

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