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

Annotations declare readOnly, openWorld, idempotent, and non-destructive. The description adds meaningful context beyond that: it is 'derived from CF analytics-engine', contains 'no PII', and is 'cached 5min-1h depending on window'. These details inform freshness and privacy expectations without contradicting the 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 longer than two sentences but each sentence earns its place: purpose, use cases, data provenance, and caching. It is well-structured and front-loaded, though slightly verbose. It could be trimmed, but the information density is high enough to justify the length.

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 simple read-only tool with one parameter and no output schema, the description covers output contents, time windows, use cases, data source, privacy, and caching. It gives the agent everything needed to decide whether and how to call this tool, making it complete for the tool's complexity.

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?

The schema fully documents the single window parameter with enum values and a description. The tool description enriches this by explaining the tradeoff: 'Shorter windows surface what's hot right now; longer windows show steady-state demand' and notes the default. This adds valuable interpretive guidance beyond the 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 opens with a clear statement of what it returns: 'top tools, top packs, and total call volume' over a recent window. The verb 'Returns' and specific data make it unambiguous, and it is clearly distinct from sibling tools like discover_tools by focusing on aggregate usage trends from other AI agents.

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 provides three explicit 'Useful for' scenarios: discovering hot data sources, confirming canonical tool choices, and checking alignment with agent needs. This gives clear context for when to use it, but it does not name alternative tools or explicitly state when not to use it.

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