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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond that: it states the data is self-aggregating, derived from CF analytics-engine, contains no PII, and is cached 5min-1h depending on window. This enriches the agent's understanding of data freshness and privacy, but doesn't fully specify response shape or potential rate limits.

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 with a lead sentence defining the tool, a 'Useful for' list, and a closing note on data source and caching. It is not overly verbose; each sentence serves a purpose. A minor deduction because the 'Useful for' list could be slightly more compact, but it is still effective.

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 and no output schema, the description is complete. It explains what the tool returns, why it exists, how to choose a window, and important behavioral traits like caching and privacy. No critical information is missing for an agent to decide on and invoke the tool.

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 input schema covers 100% of the parameter documentation, including the enum values and an explanation that shorter windows surface hot items while longer windows show steady-state demand. The description mentions the window options but does not add substantive new semantics beyond the schema, 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 starts with a specific verb+resource: 'What other AI agents are calling on Pipeworx right now' and clearly states it returns top tools, top packs, and total call volume over a window. This distinguishes it from sibling tools like get_leaderboard or discover_tools by focusing on real-time usage trends.

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 a numbered list of three concrete use cases: discovering hot data sources, confirming canonical tool choices, and checking alignment with agent needs. This gives clear context for when to use the tool, though it does not explicitly name alternative tools or when not to use it, so it stops short of a 5.

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