Evaluate text

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

With no annotations, the description carries the behavioral burden and does well: it discloses the aggregate verdict values, per-rule answers with probability and confidence, and the semantic meaning of fail and review. It does not explicitly mention persistence, auth, rate limits, or usage costs, but the core behavior is clearly and usefully disclosed.

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 compact and front-loaded: purpose first, return shape second, verdict interpretations third. Every sentence adds meaningful information with no filler or repetition.

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 3-parameter tool with no output schema, the description adequately explains return values and verdict semantics, making it callable. Minor gaps remain: it does not specify the exact JSON shape of per-rule answers or direct users to list_rule_sets for discovering available rule sets.

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 coverage is 100%, so the baseline is 3. The description adds value by explaining the rule_set concept, the default 'content-safety' set and its checks, and the intended input. This goes beyond the bare schema descriptions.

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 scores text against an Overwing rule set, with a specific verb and resource. It does not explicitly contrast itself with siblings like evaluate_batch, so it stops short of full sibling differentiation.

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 gives clear context: it is for scoring text (commonly LLM output) against a rule set, and it explains what fail and review verdicts mean for downstream action. It does not provide explicit when-not-to-use guidance or point to alternatives such as evaluate_batch, but the context is strong enough for basic selection.

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