submit_query_feedback

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

Without annotations, the description bears full responsibility for disclosing side effects. It notes that incorrect SQL 'may be promoted to a few-shot example', which is a notable behavior. However, it lacks details on reversibility, permissions, or other effects like system modifications.

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 description is extremely concise at two sentences, with the primary purpose front-loaded. No extraneous information, every sentence 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?

Given 5 parameters with full schema descriptions, no output schema, and no annotations, the description is minimally adequate. It fails to explain return values, success/error handling, or the exact conditions for promotion, though the schema covers input details.

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 has 100% description coverage, so baseline is 3. The description does not add significant semantic details beyond what is already in the parameter descriptions (e.g., correct_sql parameter already notes automatic learning).

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 'Submit feedback on generated SQL', specifying the verb and resource. It also adds context about automatic classification and promotion to few-shot examples, distinguishing it from sibling tools like evaluate_queries (which likely evaluates performance) and manage_few_shot_examples (which manages examples directly).

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?

No explicit guidance on when to use this tool versus alternatives. The description does not mention conditions, exclusions, or comparisons to sibling tools like evaluate_queries or manage_few_shot_examples.

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