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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: it explains the data source (analytics-engine), privacy (no PII), and caching behavior (5min-1h), enhancing transparency without contradicting 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 well-structured with a front-loaded summary, a concise return specification, a numbered list of use cases, and additional context. Every sentence earns its place; no unnecessary fluff.

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 covers the essential aspects: what it does, when to use it, data source, privacy, and caching. It is fully self-contained.

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 already provides a thorough description of the 'window' parameter with enums and explanations. The description text merely mentions the windows ('24h, 7d, or 30d') without adding meaning, so the schema carries the full semantic burden.

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 identifies the tool as returning trending call data ('top tools, top packs, and total call volume') with a specific time window. It distinguishes itself from siblings like discover_tools by focusing on aggregated usage signals rather than general discovery.

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 explicit use cases for when to consult this tool, such as discovering hot data sources or validating a canonical tool choice. It gives clear context but does not explicitly mention exclusions or name alternative tools.

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