rca_pyrca_ht_diagnosis

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by explaining the statistical rigor, descendant adjustment behavior, and the algorithm's purpose. No contradictions with annotations.

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 front-loaded with the core purpose, followed by recommendations, parameter details, return info, and attribution. Every sentence serves a purpose, but it is slightly verbose with academic references.

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 complexity and presence of an output schema, the description covers the main functionality but lacks details on failure modes, edge cases (e.g., insufficient data), and error handling. Adequate but not exhaustive.

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 description coverage, the description lists all key parameters with explanations (e.g., 'full time series', 'causal graph', 'p-value threshold default 0.05'). This adds meaning beyond the schema's structured 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 performs hypothesis-testing RCA with descendant adjustment (HT-ADJ). It specifies the verb 'tests whether the SLI anomaly can be statistically explained' and distinguishes itself from siblings by emphasizing statistical rigor and recommending it for well-validated causal graphs.

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 clear context: recommended when you have a well-validated causal graph and sufficient pre-anomaly data. This implies when not to use, but does not explicitly list alternatives or exclusions, leaving some ambiguity.

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