eval_answer_accuracy

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

With no annotations, the description discloses the QAG grading method, return type (score, passed, reason), and the use of a judge model. It does not mention side effects or API dependencies, but for an eval tool this is acceptable.

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, with two focused paragraphs. The first paragraph states the purpose and method, the second details parameters and return value. No filler or redundant information.

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 absence of an output schema, the description still provides the return format and explains the QAG method. It covers the essential aspects but could mention edge cases or error handling for completeness.

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?

Despite 0% schema description coverage, the description provides clear parameter semantics: expected_answer (ground-truth), actual_answer (LLM's answer), judge_model (provider:model). This compensates fully for the lack of 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 evaluates semantic equivalence of answers to ground truth using QAG, distinguishing it from strict string matching. Among sibling eval tools, this purpose is distinct and well-defined.

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 includes a clear usage guideline: 'Useful when string match is too strict (e.g. paraphrased correct answers).' It provides context but does not explicitly differentiate from other eval tools like eval_faithfulness or eval_hallucination.

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