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

With no annotations, the description carries the full burden and does substantial work: it names the underlying model (UNISAL with an AUC figure), discloses cost ('Free'), and warns about known biases ('faces draw it strongly, printed words less than people read them') plus the epistemic limit of the output. It stops short of covering authentication expectations for api_key.

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 purpose and output inventory are front-loaded in the first sentence, followed by model caveats and cost. It is dense but each clause (returns list, model identity, bias warning, CTR disclaimer, free) carries real information; the API endpoint mapping at the end is the only filler.

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?

An output schema exists, so the return-value enumeration is partly redundant, but the description supplies the missing behavioral layer (cost, model provenance, known bias, predictive-not-causal caveat) that no structured field covers. Only auth/error behavior is left unaddressed, which is a minor gap for a 3-param, all-optional analysis 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 coverage is 67%, with thumbnail and video_url already documented in the schema; the description adds nothing about parameter syntax or mutual exclusivity (e.g., whether thumbnail and video_url are alternatives). The api_key parameter is undocumented in both the schema and the description, so the description does not compensate for the gap.

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?

States a specific verb+resource: predicting where the eye lands on a thumbnail, and enumerates the concrete outputs (heatmap PNG, 16x9 grid, peak, third shares, concentration). This clearly separates it from siblings like get_thumbnails, search_thumbnails, or make_thumbnails, which generate or retrieve images rather than analyze attention.

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 line 'a prediction, not a click-through rate' and 'Free' hint at the appropriate use case, but the description never explicitly says when to pick this over siblings such as get_thumbnails or edit_thumbnail, nor does it state prerequisites or exclusions. Usage is implied rather than routed.

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