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

Annotations already declare readOnlyHint=true, destructiveHint=false. Description adds value by explaining the data source (CF analytics-engine), no PII, and caching behavior (5min-1h depending on window). 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 concise paragraphs. First sentence delivers the core function. Second block lists use cases in bullet-style. Third sentence covers caching. No wasted words, front-loaded with key information.

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 tool with one parameter, no output schema, and comprehensive annotations, the description is complete. It explains what is returned (top tools, packs, volume) and how it works (aggregation, caching). Could optionally mention output format but not needed.

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% for the single 'window' parameter with enum and description. The description adds context on choosing between windows (shorter vs longer windows surface different demand signals), which provides marginal added value 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 clearly states the tool returns top tools, top packs, and total call volume over recent windows (24h, 7d, or 30d). It uses specific verbs ('returns', 'calling on', 'discovering') and distinguishes itself from sibling tools like 'discover_tools' by focusing on trending data derived from call volume.

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 popular/canonical tools, and aligning use case with agent trends. It implies when to use each window (shorter for hot now, longer for steady-state). No when-not guidance, but the context is clear enough.

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