eval_relevance

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

With no annotations provided, the description carries the full burden. It discloses the QAG method and the return structure, but does not mention side effects, permissions, rate limits, or determinism. The behavioral disclosure is adequate but not comprehensive.

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 short, front-loaded with the purpose, and structured with Args and Returns sections. Every sentence is necessary and no extraneous information is present.

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 has 3 parameters and an output schema described in the returns, the description covers the main points: method, parameters, and return values. However, it misses explaining the threshold default and configuration, and lacks guidance on when to use this tool versus siblings. Still, it is largely complete.

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 0%, so the description must add parameter meaning. It clearly explains input as 'The user's question', output as 'The LLM's response', and judge_model with format 'Provider:model'. This adds value beyond the schema's titles and types. A slight deduction for lacking constraints on judge_model values.

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 checks if an LLM output addresses the user's question, using a QAG-graded method. This is a specific verb+resource, and the method detail (generates yes/no questions) distinguishes it from sibling evaluators like eval_answer_accuracy or eval_faithfulness.

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 for relevance checking, but does not explicitly provide when-to-use or when-not-to-use guidance relative to sibling tools. No alternatives or exclusions are mentioned, which is a gap given the presence of many similar eval_* tools.

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