ml_virtual_agent_nlu

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds that it analyzes metrics, which is consistent and non-contradictory. However, it does not disclose additional behavioral traits such as aggregation methods, data freshness, or rate limits.

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 a single sentence that gets straight to the point with no wasted words. It is concise and efficiently conveys the tool's purpose.

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 there is no output schema, the description could provide more detail about the return format or structure of the analysis. While it mentions key metrics, it is somewhat minimal for a tool that returns analytical 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?

Schema description coverage is 100%, so the schema already documents both parameters (topic_sys_id and days) with their defaults and optionality. The description adds no extra meaning beyond the schema, so baseline 3 is appropriate.

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 analyses Virtual Agent NLU performance, specifying 'conversation completion rates and fallback metrics'. It distinguishes from sibling tools that retrieve individual conversations or list topics, as it is an analytical tool rather than a retrieval one.

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 implies the tool is for performance analysis, but provides no explicit guidance on when to use it versus alternatives like 'get_va_conversation' or 'list_va_conversations'. No when-not-to-use or prerequisite information is given.

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