get_job_evaluations

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

No annotations are provided, so the description carries full burden. It discloses the returned fields (rubric, score, justification, etc.) and that a job may have multiple evaluations. It does not explicitly state it is read-only or mention error handling, but the 'return' verb implies no side effects. Adequate but not exhaustive.

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 concise (two paragraphs), front-loaded with purpose, and lists output fields efficiently. No redundant 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 no output schema, the description thoroughly explains the returned data structure (rubric, score, breakdown, justification, timestamp, provider) and multiplicity. The tool is simple (one param), and the description is sufficient for correct usage.

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?

With zero schema description coverage, the description adds meaning by specifying job_id is a UUID from list_jobs or get_job. This helps the agent know the expected format and source. The single parameter is well-documented.

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 it returns all LLM-as-judge evaluation results for a specific job, specifying the resource (evaluation results) and action (return). This distinguishes it from sibling tools like get_job (job info) and list_eval_rubrics (rubrics only).

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 explains that the tool is for querying evaluation results for a given job ID from list_jobs or get_job. It does not explicitly state when not to use it or mention alternatives like evaluate_job, but the use case is clear and straightforward.

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