run_ab_comparison

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

No annotations are provided, so the description bears full responsibility. It mentions computing metrics and returning a recommendation, but it does not disclose whether the tool modifies state, requires specific permissions, or performs any side effects. It also does not explain how the inputs are matched (e.g., by ID) or what 'statistical delta' means.

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 two sentences, front-loaded with the purpose and output. No unnecessary words; every sentence serves a purpose.

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 no output schema and no annotations, the description explains the output but lacks details on input matching (e.g., datasets must share IDs), the nature of the statistical test, and any constraints (e.g., same length of arrays). It is minimally adequate but leaves gaps.

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 100%, so the baseline is 3. The description adds the list of metrics and the fact that datasets should be the same, but does not provide additional semantic meaning beyond what the schema already offers. The mention of 'same dataset' is not reflected in the schema and could be clarified.

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 compares two model variants (A and B) using precision, recall, F1, and AUC, and returns a winner recommendation and statistical delta. This is distinct from sibling tools like detect_bias or evaluate_model_inference.

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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or contrast with siblings like detect_bias or evaluate_model_inference.

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