analyze_root_cause

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds that the analysis is 'deep' and 'AI-assisted' and enumerates the deliverables, but gives no sense of latency, cost, or data scope for what is presumably a heavyweight call, and there is no output schema to fall back on.

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?

A single front-loaded sentence with no filler; every clause names a distinct output the agent will receive. Slightly list-like but efficient for the amount of information conveyed.

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?

Because no output schema exists, the enumeration of returned artifacts is genuinely useful. But for an AI-driven analysis call sitting among several overlapping analysis siblings, the definition leaves the agent without selection criteria or any hint of cost/duration, so it is only adequate, not complete.

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 description coverage is 100%: jobId format, environment lookup via list_environments, and lookbackHours default/max are all documented in the schema. The description adds no parameter-level meaning beyond that, so the baseline 3 applies.

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?

States a specific verb and resource ('Deep AI-assisted root-cause analysis') and enumerates what it produces: failure details, abend reference, incident correlation, health score, action items. However, it draws no boundary against the near-identical siblings analyze_job_failure and investigate_incident, so an agent cannot tell from the description which analysis tool to pick.

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?

There is no when-to-use guidance at all. With three overlapping failure-analysis siblings in the toolset, the absence of any routing rule or prerequisite (e.g., 'run after get_job_status confirms failure') is a meaningful gap; only the required jobId parameter implicitly signals it applies to a failed job.

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