run_check

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

With no annotations provided, the description fully carries the burden. It describes the return statuses and their meanings, observability signals, auto-detection of test_path, behavior of heal=true, and the recommendation to treat REGRESSION as blocking. It also explains flaky failure handling and the strict flag's effect.

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 somewhat lengthy but well-structured: it starts with the core result summary, then usage guidelines, then important notes like test_path detection. Every sentence adds value, though it could be slightly more concise without losing 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 tool's complexity (14 parameters, no output schema, 0 required params), the description covers return values, usage context, parameter behavior, and includes an important automated behavior (test_path detection). This provides sufficient context for an agent to use the tool correctly.

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?

All parameters have schema descriptions (100% coverage). The description adds extra context beyond the schema, such as auto-detection logic for test_path and the purpose of heal for distinguishing non-determinism. This adds value but does not compensate for missing schema descriptions since none are missing.

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 checks for regressions against a golden baseline and returns diff summaries with specific statuses (PASSED, REGRESSION, etc.). It also lists observability signals. This distinguishes it from sibling tools like run_skill_test (which likely runs tests without baseline comparison) and replay (which replays interactions).

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 advises using this tool 'after any code change (prompt, model, tools) to confirm nothing broke.' It also instructs to show diff and offer to fix if regression is found. However, it does not explicitly contrast with alternatives among sibling tools, such as when to use run_skill_test instead.

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