submit_feedback

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

With no annotations present, the description carries the full disclosure burden and does add real value: it reveals that feedback is saved locally and forwarded to a central feedback loop 'when available,' which is the kind of side effect an agent cannot get from the schema. It does not cover authorization, idempotency, or whether a query_id can be reused.

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 short sentences, front-loaded with the action and followed immediately by the 'learning signal' framing and the negative-case rule. No filler, no repetition of the schema.

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 three-parameter tool with no output schema this is close, but the gap on query_id provenance is material: without knowing it comes from a prior search_documents/answer_question call, an agent may pass an arbitrary or stale identifier. The return behavior and consequence of submission are also unstated.

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 0% with only bare titles, so the description must compensate and it partly does: it defines the semantics of helpful (the false branch specifically) and of note (a short note on what was missing). The required query_id, the most important linkage parameter, is never explained — where it comes from or what format it takes.

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 opens with a specific verb and object: report whether the results for a query_id answered the question, and labels itself 'the learning signal.' That is far more than a restatement of 'submit_feedback' and clearly separates it from retrieval siblings like search_documents and answer_question. It stops short of naming those siblings explicitly, so it does not fully route the agent.

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 a concrete negative-case trigger: use helpful=false with a short note when results were empty, irrelevant, or outdated. That is actionable guidance an agent can apply without inference. It does not state when-not to call it at all, nor point to related siblings such as list_knowledge_gaps or recent_failures.

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