interview_story_suggest

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

With no annotations, the description carries the full burden of behavioral disclosure. It does not reveal important traits such as behavior when no stories match, potential performance implications, or whether the operation depends on prior data loading, limiting transparency.

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 single-sentence description is efficient and directly conveys the core purpose. While it is not excessively brief, it sacrifices some helpful details, earning a high score for conciseness with minor room for improvement.

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 low complexity (2 params, no nested objects) and the presence of an output schema, the description covers the essential action adequately. However, missing usage guidelines and behavioral traits limit its completeness for an agent judging when to invoke it.

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?

Although the schema already describes both parameters, the tool description adds critical value by specifying that the match is partial and case-insensitive. This clarifies the query semantics beyond the schema's generic 'The competency to look for.'

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 returns stories matching a competency, with explicit details on partial and case-insensitive matching. It effectively distinguishes from sibling tools like interview_story_add, which creates stories, by focusing on retrieval.

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 guidance is provided on when to use this tool versus alternatives such as interview_story_add or interview_question_bank. The description lacks context about prerequisites or typical use cases, leaving the agent to infer indirectly.

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