score_dataset_preview_candidates

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

No annotations are provided, so the description must disclose behavioral traits. It only states it operates as a 'dataset-level decision set' but does not mention side effects, required permissions, or whether it records data. The agent gains minimal understanding of the tool's impact beyond its primary action.

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 a single short sentence, which is concise, but it sacrifices clarity and completeness. It contains no wasted words, but the brevity undermines its utility. A more structured description could provide the same conciseness with added 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 the tool complexity (5 parameters, many sibling tools, no annotations), the description is inadequate. It fails to explain input semantics, output structure, or behavioral context. An output schema exists but is not leveraged in the description. The description is too sparse to support correct agent decision-making.

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 description coverage is 0%, yet the description adds no parameter explanations. Of the 5 parameters (baseline_run_id, candidate_run_ids, feedback_tags_by_candidate, accepted_candidate_ids, quality_profile), only 'candidate_run_ids' is vaguely implied. The enum for quality_profile and optional parameters for feedback and acceptance are completely unaddressed.

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?

Description states 'Score several preview candidates as one dataset-level decision set', clearly indicating a batch scoring operation on preview candidates. It distinguishes from sibling tools like 'rank_preview_candidates' by emphasizing the dataset-level decision aspect, but the verb 'score' is somewhat ambiguous without further context.

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 on when to use this tool versus alternatives such as 'compare_preview_runs' or 'record_preview_feedback'. The description does not specify prerequisites, typical workflows, or exclusions, leaving the agent to infer usage context from the name alone.

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