verify_regression

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

With no annotations, the description should disclose more, e.g., what happens on mismatch, error handling, or side effects. It only says 'comparing their outputs', which is vague. However, the core action is clear.

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, 18 words, no filler. Front-loads the action and purpose. Every word earns its place.

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?

For a tool with two simple parameters and an output schema, the description is largely sufficient. It could mention result handling (e.g., what constitutes a regression), but overall complete given the tool's simplicity.

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 100%, so baseline is 3. The description adds overall context but doesn't enhance understanding of individual parameters beyond the schema's 'Shell command to run...' 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 runs a legacy and modern binary, then compares outputs to detect regressions. This is a specific action that distinguishes it from sibling tools like run_tests, which focus on unit tests.

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 implicitly tells when to use (after refactoring to verify no regressions), but does not explicitly state when not to use or mention alternatives like run_tests. Still, the context is clear.

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