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

Annotations already mark it as read-only and idempotent, and the description adds important behavioral context: it's derived from CF analytics-engine, contains no PII, and is cached for 5min-1h. This goes beyond the schema and helps set expectations about data freshness and aggregation.

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 compact but informative, leading with the core function and supplementing with a concise bullet list of use cases. No irrelevant details are present.

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, the description covers purpose, output, caveats about caching and PII, and usage guidance. The lack of an output schema is compensated by the explicit listing of return contents.

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 only parameter 'window' has a rich schema description including enum and semantics for choosing windows. The tool description repeats the window options but doesn't add new meaning beyond the schema, so it meets the baseline for high schema coverage.

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 it returns trending data from Pipeworx, specifying output as top tools, top packs, and total call volume. This distinguishes it from sibling tools like discover_tools which focuses on discovery. The verb 'Returns' is specific and accurate.

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 three explicit use cases, covering when to use the tool for discovering hot data sources, validating canonical choices, and aligning with agent needs. It lacks explicit exclusions or alternative tool references, but gives clear context for adoption.

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