get_token_analytics

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, establishing safety. The description adds value by disclosing the enterprise gating and the 403 error response on failure, which goes beyond 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?

Two sentences, each serving a distinct purpose: the first defines the tool's function and output; the second provides usage guidance, sibling distinction, and access constraints. No unnecessary words.

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 29 parameters, 100% schema coverage, output schema, and rich annotations, the description covers core purpose and a key sibling. However, it lacks details on time-series bucket granularity, pagination, or ordering. Still, the structured fields and schema compensate partially.

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 parameters are already well-documented. The description does not add extra meaning to specific parameters but clarifies the overall output structure (summary fields, per-bucket counts). 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 retrieves token-usage time-series data with specific metrics (summary.total_tokens, summary.prompt_tokens, summary.completion_tokens, per-bucket counts). It also distinguishes from the sibling 'get_cost_analytics' by noting the difference between token volume and spend.

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 says 'Use this for consumption trends' and suggests 'use get_cost_analytics when you need spend instead of token volume'. It also notes the enterprise gating and 403 error for non-enterprise plans, providing clear context for when the tool is applicable.

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