eval_context_recall

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

No annotations exist, so the description carries full burden. It explains the judge's role: 'asks whether the expected answer could plausibly be reconstructed from the retrieved context alone.' It also defines 'high recall.' No destructive side effects are expected, and the behavioral disclosure is adequate.

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 well-organized: a short summary, a clarification paragraph, an explicit usage line, and an Args section. It is efficient without being wordy, though it could be slightly more compact.

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's complexity and the presence of an output schema, the description covers all essential aspects: purpose, usage context, parameter meanings, and the return structure (score, passed, reason, threshold, evaluator). This is fully sufficient for correct agent invocation.

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 compensate. It lists all four parameters (input, context, expected_answer, judge_model) with brief but clear explanations. For example, it notes that context can be a list or single string. This adds meaning beyond the raw schema.

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 starts with a clear one-sentence purpose: 'Measure whether retrieved context contains enough information to answer.' It specifies the resource (retrieved context) and the action (measuring recall). The context of retriever vs. generator diagnosis distinguishes it from sibling tools like eval_context_precision 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?

Explicitly states when to use: 'when you have a labelled QA dataset and want to diagnose whether failures are retriever misses vs. generator errors.' It clearly sets the context but does not explicitly mention alternatives or when not to use it, leaving some room for improvement.

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