get_token_analytics

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

Annotations already declare readOnly, destructive, and idempotent hints. The description adds value by disclosing the enterprise-gating and 403 error behavior, and summarizing the data shape (summary + per-bucket counts). No contradictions.

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?

Three sentences, no redundancy, front-loaded with the core action and data shape. Every sentence provides essential information without waste.

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 the high parameter count (29) and existence of an output schema, the description covers the key behavioral aspects (purpose, alternatives, access restrictions). It could briefly note that time range parameters are required, but overall 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?

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-level details beyond what the schema provides, but the schema is thorough, making this adequate.

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 precisely states it retrieves token-usage time-series data with specific summary fields and per-bucket counts. It distinguishes itself from the sibling get_cost_analytics by contrasting token volume vs. spend, making the tool's unique purpose clear.

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?

Explicitly advises to use this tool for consumption trends and directs to get_cost_analytics for spend analysis. Also notes enterprise-gating and a 403 response for non-Enterprise plans, providing clear usage context and constraints.

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