linkedin_get_remembered_answers

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the context that answers are from previous applications and can be reviewed/corrected, but it does not disclose potential staleness, pagination, or whether answers are per-company or global. With strong annotations, a 3 is fair.

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?

Single sentence, fully front-loaded, with no filler. It clearly states the action and purpose. Slightly more detail on the output (e.g., 'answers' structure) could earn a 5, but as it stands it is concise and effective.

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 zero-parameter read tool with strong annotations, the description is mostly complete. However, it lacks specifics about what fields are returned or how corrections might be applied (e.g., via a separate tool like linkedin_forget_answer). The output schema is absent, so a bit more detail on return value would improve completeness.

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 tool has zero parameters, so schema coverage is 100%. The description explains the resource being listed, which is useful for understanding what the empty parameter set means. Baseline for 0 params is 4, and the description adds minimal but adequate context.

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 tool lists remembered screening-question answers for review/correction before next batch, using specific verb+resource. It doesn't explicitly distinguish from sibling linkedin_forget_answer, but the purpose is clear and unique enough among the large sibling list.

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 implies usage context (review/correct before next batch) but does not explicitly mention alternatives or when not to use. Given the sibling tool linkedin_forget_answer likely manages these answers, some guidance would help but is not critical for a simple read operation.

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