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

Annotations declare non-read-only, idempotent, non-destructive; the description adds things annotations can't convey: the call costs nothing, the run must already be finished, and ratings aggregate into list_models stats and model:"auto" selection. It stops short of saying what happens on re-rating an already-rated run or on an unknown run_id, so it's strong but not exhaustive.

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, front-loaded with the operation and scale, then triggers, then the incentive rationale. No sentence is redundant and nothing is buried.

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 a three-parameter, non-destructive feedback tool with no output schema, the description covers cost, preconditions, triggers, and consequences of the data. An agent has everything needed to decide to call it and how to score honestly.

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%, and the schema already defines the score anchors (1 unusable, 3 usable with edits, 5 shipped) and the note's one-line purpose, so the baseline is 3. The description adds only scoring-conduct framing ("an honest 2 helps more than a polite 4") rather than new format or constraint detail.

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?

Names a specific verb (Rate) and resource (a finished generate_video/generate_image/generate_audio run), plus the 1-5 scale and the optional note. This cleanly distinguishes it from the generation siblings, which create runs rather than evaluate them.

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?

Explicit triggers are given: the user reacts to an output (with quoted examples like "her face changed" or "too slow"), or the agent itself observes a defect. It also names the downstream consumer of the data, which tells the agent why and when the call matters.

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