get failure clusters

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

Beyond the readOnly/idempotent annotations, the description discloses the hourly cache and how 'force' bypasses it, the metered model-call billing on fresh clustering, deduplication of online FAILs and pending suspects, the 'without_reason' exclusions from clustering, and the 400 error for invalid window_days. These are meaningful behavioral details that annotations alone cannot convey.

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 long but dense and well ordered: purpose, endpoint/scoping, full return shape, and then cache/error/billing notes. Every section earns its place because there is no output schema to rely on. Minor redundancy like 'Cache-Control: no-store' could be dropped, but it does not detract much.

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?

With no output schema, the description fully specifies the response structure, including the cache flag, criteria array, cluster fields, and the representative rationale example. It also covers error behavior, billing, and edge cases such as criteria with fewer than four reasons. An agent has everything needed to call the tool and interpret results.

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?

The schema already documents both parameters fully (defaults, ranges, and force's cache-bypass effect), so the baseline is 3. The description adds further value by specifying the cost implications of force=true, the 40-reason cap per criterion, and the exact 400 error message when window_days is out of range.

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 names a precise resource ('live production failures grouped into systemic causes per criterion') with a clear action ('See') and the user purpose ('find what to fix first rather than reading failures one by one'). It is not a tautology of the title and is easily distinguishable from sibling tools that scan or list individual failures.

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 clear context: use this to get an aggregated, prioritized view of production failures instead of inspecting them individually. It does not explicitly name an alternative tool or a when-not-to-use condition, but the intended scenario is unmistakable.

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