analyze_image

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

With no annotations provided, the description carries the full burden, and it does well: it discloses the underlying vision model (deepseek-v4-flash), accepted input forms (local paths or URLs), constraints (1–4 images, ≤20MB, specific formats), and the return type (text answer). It does not mention error handling or rate limits, but the core behavioral traits are transparent.

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 a single dense paragraph that front-loads the purpose, then provides usage conditions, then parameter details, and finally the return type. Every sentence carries information; it is slightly long but well-organized and free of fluff.

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 when to use, input constraints, parameter semantics, and return type, which is comprehensive for a tool of this simplicity with no output schema. It omits failure scenarios and exact formatting of the returned text, but nothing essential is missing for an agent to invoke it 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 coverage is 100%, so the baseline is 3. The description adds meaningful value beyond the schema by explaining that 'images' can be local absolute paths or URLs, with size and format limits, and that 'question' is a question for the vision model. This is practical usage context that the schema alone does not convey.

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 states a specific verb ('analyze image content using a vision model') and a clear resource (images + question). It enumerates concrete use cases — understanding screenshots/charts/photos, OCR, comparing multiple images — which fully clarifies what the tool does. There are no sibling tools, so no differentiation is needed, and the purpose is unambiguous.

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 explicitly states when the tool must be used: whenever the current model lacks vision capability or needs to interpret image content, perform OCR, or compare images. This is a direct 'call this tool when...' instruction, giving the agent a clear decision rule even without sibling tools to distinguish from.

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