Trigger AI Root Cause Analysis Generation

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

With no annotations, description fully discloses destructive nature (spends real credits, cannot be undone), automatic skipping of existing/generating RCAs, mixed batch routing, all-or-nothing dispatch on insufficient credits, and rejection of scopes >10,000 failed tests. Highly transparent.

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?

Description is long but every sentence adds value. Front-loaded with main action, then details constraints and dangers. Could be slightly more concise, but structure is logical and complete.

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, description explains return values (new triggers vs skipped, credits used). Covers error cases (402, 413) and safety checks. Complete for a complex, high-risk tool.

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?

Despite 0% schema coverage, description explains each parameter (job_ids, stage_ids, etc.) as arrays capped at 100 IDs, with at least one required. Adds context on how test_ids route automatically. Could be more explicit about ID format, but compensates well for missing schema 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 it dispatches AI-powered RCA generation for failed tests under a given scope. The title 'Trigger AI Root Cause Analysis Generation' is specific. It distinguishes from sibling tools like tm.get_testExecutionRCA by focusing on generation rather than retrieval.

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?

Provides explicit when-to-use: for failed tests needing RCA. Also states when-not-to-use: not speculatively or on broad scope. Advises using tm.get_testExecutionRCA first to check existence. Warns about credit costs and all-or-nothing behavior, guiding safe usage.

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