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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context: data is derived from CF analytics-engine, no PII, cached 5min-1h depending on window, and self-aggregating signal. This goes beyond annotations but could still mention rate limits or pagination. No 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 (about 6 sentences), well-structured with front-loaded core function and bulleted use cases. Every sentence adds value without redundancy or unnecessary detail.

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 optional parameter and no output schema, the description is complete. It covers data source, privacy, caching, and use cases. Annotations further enrich the context. No gaps identified.

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% with a clear enum description for the only parameter 'window'. The description adds meaning beyond the schema by explaining the trade-off: shorter windows surface current hotness, longer windows show steady-state demand. This helps the agent choose appropriately.

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 a window, using specific verbs like 'Returns' and 'calling'. It distinguishes from siblings by focusing on trending/aggregate data, which is unique among a large set of sibling tools.

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 explicitly lists three distinct use cases (discovering hot data sources, confirming canonical choice, aligning with needs). It also provides context on caching and privacy. However, it does not explicitly state when not to use this tool or mention alternatives, which prevents a perfect score.

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