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

Annotations already indicate safe, read-only, idempotent behavior. The description adds useful context: caching window (5min-1h), data source (CF analytics), no PII, and self-aggregating nature. This enriches understanding beyond annotations without contradiction.

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 three short paragraphs: core function, use-case bullets, and technical details. It is front-loaded and every sentence adds value. No redundancy or filler.

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 read-only trend tool with simple input and rich annotations, the description covers purpose, use cases, caching, and data source. However, it lacks a detailed output format specification (e.g., structure of returned objects), which would aid agents in parsing results. Still, it provides enough for basic invocation.

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 coverage is 100% and provides enum values with descriptions. The tool description adds extra context about window behavior (shorter for hot, longer for steady-state), which enhances meaning beyond the schema. This justifies a score above the baseline of 3.

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 'top tools, top packs, and total call volume' for a given window. It distinguishes itself from sibling tools like discover_tools by focusing on what other agents are calling, not general discovery. The verb 'returns' and specific resource make 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?

Three explicit use cases are given (discovering hot data sources, confirming canonical choice, seeing alignment). No exclusions or alternatives are mentioned, but the context is clear enough that an agent can infer when to use it. Lacks explicit when-not-to-use guidance.

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