get_guardrail_categories

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

No annotations are provided, so the description carries the full behavioral burden. 'Counts' implies a read-only aggregation, but nothing is said about the time window, whether results are pre-aggregated or live, rate limits, or return shape for a tool with no output schema.

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?

One sentence, front-loaded with the resource and the aggregation, with the optional scoping trailing. No waste, though it is arguably under-specified rather than concise.

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 annotations and no output schema, the description should explain the time window and the returned count structure, neither of which is covered. It is minimally adequate for a one-parameter aggregation but leaves key operational context to inference.

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 0% for the single agent_id parameter, so the description must compensate. It does add meaning by explaining the parameter is an optional scope ('optionally scoped to one agent'), clarifying it filters rather than requires, but gives no format or default behavior detail.

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 resource and measurement ('guardrail-detector-category counts') plus the population it covers ('across LLM interactions'), so the agent knows this is an aggregation query. It does not distinguish itself from the near-identical sibling get_guardrail_interaction_counts, leaving ambiguity about which counting 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 and no named alternative, despite several overlapping guardrail siblings (get_guardrail_outcomes, get_guardrail_interaction_counts). The only hint is 'optionally scoped to one agent', which describes the parameter rather than the use case.

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