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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds non-obvious behavioral details: 'derived from CF analytics-engine, no PII, just (pack, tool, count)' and 'Cached 5min-1h depending on window.' This goes beyond the annotations to inform the agent about data freshness, privacy, and source, which is valuable context.

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 somewhat lengthy but well-organized: it opens with the core value proposition, lists use cases in a numbered format, and ends with data provenance and caching details. Every section adds useful context, though the use-case list could be slightly tightened without losing value.

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 is simple (one optional parameter with enum), and the description fully explains its output ('top tools, top packs, total call volume') and behavior (cached, no PII) without an output schema. It provides enough context for an agent to select and invoke the tool correctly, and its use cases cover the main scenarios.

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) with an enum and description. The tool description adds marginal nuance by explaining that shorter windows surface 'what's hot right now' and longer windows show 'steady-state demand,' but this largely mirrors the schema's description. The baseline of 3 applies because schema coverage is 100%.

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: 'What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window.' This distinguishes it from sibling tools like discover_tools by specifying it reveals aggregated usage by other agents, not a general discovery mechanism.

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 lists three specific use cases: discovering hot data sources, confirming canonical tool choice, and aligning with typical agent needs. This provides clear context for when to use the tool, though it does not explicitly mention when not to use it 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.