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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description adds valuable behavioral context: caching (5min-1h), data source (CF analytics-engine), no PII, and self-aggregating signal. No contradiction with annotations; this is fully additive transparency.

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: a leading summary, a numbered list of use cases, and a final technical note. Every sentence adds value and contributes to the overall understanding without redundancy or filler.

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 tool with one optional parameter, no output schema, and strong annotations, the description covers return content, use cases, caching behavior, privacy, and data provenance. There are no obvious gaps in what an agent would need to know to invoke it correctly.

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 description coverage is 100% and includes the nuanced distinction between short and long windows. The tool description merely restates '24h, 7d, or 30d' without adding new semantic meaning, so the baseline of 3 applies.

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 uses a clear verb ('Returns') with specific resources ('top tools, top packs, and total call volume') over a time window. It distinguishes itself from siblings like discover_tools by emphasizing cross-agent aggregate behavior ('What other AI agents are calling'), making the purpose unambiguous.

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?

Provides explicit use cases (hot data sources, canonical tool confirmation, use-case alignment) that tell the agent when to invoke it. However, it does not explicitly name when-not-to-use or alternative tools, so it falls one step short of the most explicit guidance.

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