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

Annotations already provide readOnlyHint and idempotentHint. The description goes well beyond by detailing the exact output shape (flat series, two series names, no uniqueTotal, addons structure), the ignored filters, and empty responses for invalid ranges. No contradiction with annotations.

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 front-loaded with key distinguishing shape information and provides efficient, detailed explanations of output structure, parameter behavior, and edge cases. The URL adds minor redundancy but overall every sentence earns its place.

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 no output schema, the description thoroughly explains the response structure, series names, calculation requirement, and parameter constraints. It covers all necessary details for an agent to correctly invoke and interpret the tool.

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?

Input schema coverage is 100% with descriptions for all three parameters. The description adds that only these parameters work and that aggregation defaults to week, but does not provide deeper semantics beyond the schema. Baseline score of 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 returns time-series data for cloud evaluation-to-paid conversion. It distinguishes its shape from sibling tools (churn/renewal) by specifying flat series structure, making the purpose unambiguous.

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 explains that the caller must compute the conversion rate from two series and notes that only aggregation/startDate/endDate work while other filters are ignored. It also covers edge cases like reversed or future-only ranges. While it does not explicitly name alternatives, the shape contrast with churn/renewal implies when to use this tool.

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