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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds useful behavioral details: data derived from CF analytics-engine, no PII, cached 5min-1h, which exceeds annotation requirements.

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?

Description is compact (3 sentences + bullet points), front-loaded with the main purpose. Could be slightly more structured but every sentence adds value without verbosity.

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?

With no output schema, description adequately explains return type (top tools, top packs, total call volume) and caching behavior. All necessary context for a simple read-only tool is present.

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 description adds value by explaining the trade-off between windows: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enriches the enum definition.

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?

Description clearly states it returns 'top tools, top packs, and total call volume' with specific verb 'Returns'. It distinguishes itself from sibling tools like 'ask_pipeworx' and 'discover_tools' by focusing on aggregate trending 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 (e.g., 'discovering what data sources are hot', 'confirming a popular tool...'), providing clear context for when to use. Does not mention when not to use, but the use cases sufficiently guide selection.

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