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

Beyond the read-only and idempotent annotations, the description discloses important behavioral traits: it is a 'self-aggregating signal' derived from 'CF analytics-engine', it contains 'no PII', and it has a cache window ('Cached 5min-1h depending on window'). This adds meaningful context about data provenance, privacy, and freshness that annotations do not cover.

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 slightly longer than minimal, but every sentence adds value: purpose, outputs, use cases, data source/privacy, and caching. It is well-structured and front-loaded with the main verb and resource, making it easy to scan. It earns a 4 rather than 5 because the use-case list could be tightened without losing essential guidance.

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?

Given a single optional parameter and no output schema, the description is complete: it explains what is returned (top tools, top packs, total call volume), the temporal windows, the aggregating behavior, privacy, and caching. It leaves no critical ambiguity for a safe, read-only 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 schema covers 100% of the parameter, including its default and the trade-off between shorter and longer windows. The tool description merely restates the window options ('24h, 7d, or 30d') without adding new meaning beyond the schema, 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 opens with a specific verb ('Returns') and clearly identifies the resource: 'what other AI agents are calling on Pipeworx right now' plus the concrete outputs (top tools, top packs, total call volume). This distinguishes it from siblings like discover_tools by focusing on agent call volume and trend aggregation rather than tool discovery.

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?

It provides three explicit use cases ('Useful for: (1) discovering... (2) confirming... (3) seeing whether...'), which gives clear context on when to invoke this tool. However, it does not name alternative tools or state when not to use it, so it stops short of full exclusionary guidance.

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