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

Beyond the annotations, the description discloses important side effects: it writes only to community feedback, never to the KG, does not trigger search or an LLM call, and awards visible contribution points for site_suggestions. This is exactly the behavioral context an agent needs and is not redundant with the annotations.

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?

Every sentence earns its place: the first front-loads the trigger and constraint, the second lists valid use cases and incentives, and the third captures side effects and exclusions. No filler or repetition.

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?

Given the output schema exists and the input schema fully documents parameters, the description supplies the remaining context: when to call, what counts as valid feedback, side effects, and when not to call. An agent can decide and invoke this tool correctly without further documentation.

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 input schema already has 100% coverage and documents all parameters, including enums, defaults, and the contribution-points behavior of site_suggestion. The description echoes that information without adding new parameter-level semantics, so the baseline 3 applies.

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: submit at most one short, lawful public Agent observation for specific task contexts, and enumerates valid use cases. It is not a tautology and differentiates from generic feedback by scoping to post-task observations, but it does not explicitly contrast with the sibling submit_agent_observation.

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?

It gives explicit trigger conditions ('After a real search, entity read, comparison, or grounded page-verification task') and explicit negative conditions ('Do not call it for initialization, discovery, listing, ping, repeated comments...'). It lacks named alternative tools, so it falls short of a full 5.

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