get_agent_usage_detail

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

No annotations are provided, so the description carries the full burden. It implies a read operation by stating 'Return detailed LLM inference usage' and describes the output (daily breakdown, cost trend). It does not explicitly declare read-only or disclose other behavioral traits like auth requirements 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 concise, front-loaded with the primary action, lists output details, and includes a clear 'Args' section. Every sentence adds value with no redundancy.

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?

For a single-parameter tool with no output schema, the description adequately explains what the tool does and what it returns (daily breakdown, model distribution, cost trend, recommendations). It could specify if data is historical or real-time, but overall it is sufficient.

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?

With 0% schema description coverage, the description compensates by explaining the parameter 'agent_id' as 'The agent identifier (as reported via X-VibOps-Agent-Id header).' This adds meaningful context beyond the schema's minimal title.

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 uses specific verbs ('Return detailed LLM inference usage') and resources ('for a specific agent'). It clearly distinguishes from sibling tool 'get_agent_usage' by stating 'Use after get_agent_usage identifies an agent of interest.'

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 explicitly states the tool should be used after 'get_agent_usage' identifies an agent, providing clear context. However, it does not mention when not to use it or alternative tools beyond that.

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