llm_quality_report

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 explains that the tool analyzes routing decisions over a period and shows specific metrics. However, it does not disclose if the report is read-only or if there are any side effects, but given it's a report, the transparency is adequate.

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, with a single introductory sentence and a clear Args section. Every sentence adds value without 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?

The description covers the tool's purpose and parameters. Since an output schema exists (as per context), the return values are likely defined externally. The description is sufficient given the tool's simplicity.

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?

The only parameter, 'days', has a default of 7. The description explains its purpose: 'Number of days to include in the report.' Since schema description coverage is 0%, the description fully compensates by providing clear semantics.

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's purpose: 'Show routing quality metrics — classification accuracy, savings, model distribution.' This is a specific verb+resource combination, and it distinguishes from siblings like llm_analyze or llm_health.

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 usage for reviewing routing decisions but does not provide explicit guidance on when to use this tool versus alternatives like llm_route or llm_savings. No when-not-to-use or alternative tool suggestions.

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