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

With no annotations, the description carries the full burden and explicitly discloses a significant behavioral side effect: 'Adjusts neural weights and reputation.' This goes beyond a simple 'rate' verb and warns the agent of state changes, though it does not mention reversibility or permissions.

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?

Two concise sentences deliver the core action, timing, scale, and side effect with no filler. The information is front-loaded and every clause adds value.

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 simple rating tool with no output schema, the description covers the essential elements: what, when, and consequence. It does not describe the return value or required parameter relationships, but the schema provides the required fields. The description is adequate for a basic action.

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?

The schema has 0% description coverage, so the description must compensate. It adds the meaning of the rating scale ('1-5 stars'), but fails to explain rated_did, rater_did, or feedback beyond their names. The partial compensation justifies a middle score.

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 clearly states the action ('Rate') and the target ('an agent after interaction'), with a specific scale (1-5 stars). It distinguishes itself from the quantum_* siblings by focusing on the post-interaction rating act, not on queries or deals.

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 a clear usage context ('after interaction'), implying when the tool is applicable. It does not explicitly mention when not to use it or name alternatives, but the context is sufficient for most cases.

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