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

Annotations already declare readOnlyHint, idempotentHint, and no destructiveness. Description adds valuable context: data source (CF analytics-engine), privacy (no PII), caching (5min-1h), and output structure (pack, tool, count). 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?

Two paragraphs with clear structure: first sentence states purpose, then bullet points list use cases. Every sentence is relevant, no waste, key info front-loaded.

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 tool with 1 optional param and no output schema, the description covers return values, aggregation method, privacy, and caching. Fully adequate for an AI agent to understand and use correctly.

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 has 100% coverage with description of window parameter and its default. Description adds nuance: shorter windows surface hot trends, longer windows show steady-state demand, enhancing the meaning beyond schema.

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 it returns top tools, top packs, and total call volume over a window, specifying the verb 'Returns' and the resource. It distinguishes from siblings like 'discover_tools' by focusing on current usage data.

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 lists three use cases (discovering hot data, confirming canonical choice, aligning use case). Does not provide exclusions or alternatives, but the use cases are clear and context-rich.

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