Analyze an image with a vision model

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

No annotations are provided, so the description carries the full burden. It clearly indicates non-mutating analysis behavior (understand/explain/describe) and clarifies the tool is for reasoning rather than exact extraction. It doesn't mention model defaults or fallibility, but given the output schema exists, the main behavioral traits are adequately conveyed.

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?

Three sentences: state purpose, give positive use cases, give negative use case with alternative. Front-loaded and every sentence serves a clear function with zero waste.

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?

For an analysis tool with a rich schema and output schema present, the description covers purpose, usage, and exclusion. It doesn't need to explain return values because the output schema handles that. Complete for an AI agent to select and invoke correctly.

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 100%, so the baseline is 3. The description adds a small semantic hint about prompt framing ('understand, explain, or describe') but doesn't significantly enhance parameter understanding beyond the schema's existing descriptions.

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 first sentence uses a specific verb-resource combination ('Understand, explain, or describe an image') and explicitly distinguishes from the sibling tool vision_ocr by stating 'Not for verbatim text extraction (use vision_ocr)'. This makes the purpose crystal clear and differentiates it from alternatives.

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 states concrete use cases ('screenshots, UI, diagrams, charts, or error messages that need reasoning') and explicitly excludes OCR with a direct reference to vision_ocr. This is an ideal when/when-not/alternatives pattern.

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