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

Annotations declare read-only, open-world, and idempotent hints. The description adds meaningful context: data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h). It does not detail return structure, but the description's additional detail goes beyond annotations without 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 well-structured: core function first, then actionable use cases, then technical notes. Every sentence adds value, and the length is appropriate for the tool's simplicity.

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 one-parameter read-only tool with strong annotations and full schema coverage, the description is complete. It covers purpose, use cases, data source, privacy, caching, and the nature of the returned data, making it sufficient for an agent to decide when and how to use it.

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 covers the only parameter 'window' completely, including enum values and a description of effect. The tool description merely mentions the window options and adds no extra semantic detail beyond what the schema already explains, so a baseline of 3 is appropriate.

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's function: 'Returns the top tools, top packs, and total call volume' over a time window. It distinguishes itself from siblings by focusing on aggregate trending data from other AI agents, not individual actions or queries.

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 concrete use cases, such as discovering hot data sources and validating canonical choices before asking questions. It provides clear context but does not explicitly name alternatives or exclusion criteria, though the use cases imply when it is appropriate.

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