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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description discloses meaningful behavioral traits: the data is cached for 5min-1h depending on window, it is derived from CF analytics-engine with no PII, and the output is limited to (pack, tool, count). This adds valuable context about freshness, provenance, and privacy.

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 front-loaded with the core purpose and then provides a list of use cases and a note on the data source/caching. While the 'Useful for' list is slightly lengthy, every clause adds useful information, and the overall structure remains focused and efficient.

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?

The tool has a single optional parameter, strong annotations, and no output schema. The description covers the return content (top tools, top packs, total call volume), window selection, data source, caching, and privacy, making it complete for the tool's complexity and leaving no critical gaps.

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, including the enum values, default (24h), and the semantic difference between shorter and longer windows. The tool description restates this information but does not add any additional meaning beyond what the schema already covers, so a baseline 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 what the tool does: 'Returns the top tools, top packs, and total call volume' from what other AI agents are calling on Pipeworx. It uses a specific verb ('returns') and resource, and the framing around trending agent activity distinguishes it from sibling tools like discover_tools or search_datasets.

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 use cases: discovering hot data sources, confirming a popular tool is the canonical choice, and seeing alignment with agent needs. It provides clear context for when to use the tool, though it does not explicitly state alternatives or exclusion criteria.

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