compare_to_baseline

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

No annotations are provided, so the description must disclose behavioral traits. It states the tool computes and renders a table, implying a non-destructive read operation, but does not explicitly confirm no side effects, required permissions, or details on return format (though output schema may cover that). This is adequate but minimal.

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?

Single sentence, front-loaded with the core action, no redundant words. Every part contributes to the 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?

The description explains what the tool does and the meaning of inputs. Given an output schema exists, return value details are likely covered. However, it lacks usage context (when to call) and does not mention that this is a read-only operation, which would help 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?

With 0% schema description coverage, the description compensates by labeling 'metrics_ft' as fine-tuned and 'metrics_base' as baseline, adding meaning beyond the parameter names. It clarifies the relationship between the two parameters, which is essential for correct invocation.

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 action: 'Compute per-metric deltas between fine-tuned and baseline and render a Markdown comparison table.' It specifies both the computation and output format, and the verb 'compute' and 'render' with specific resources (deltas, table) leave no ambiguity.

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?

No guidance on when to use this tool versus alternatives. With over 60 sibling tools, including 'compute_metrics' and 'evaluate_on_validation_set', the lack of context or exclusion conditions forces the agent to rely on heuristic matching.

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