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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses behavioral traits: self-aggregating, derived from CF analytics-engine, no PII, caching behavior (5min-1h), and output structure (pack, tool, count). This adds valuable context not present in structured data.

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 a clear opening sentence, followed by a well-structured list of use cases and a final note on data characteristics. It is slightly verbose but every sentence contributes useful information, earning a strong score.

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 read-only tool with one optional parameter and no output schema, the description is thorough: it clarifies what is returned, why it is useful, how data is sourced, and its freshness. No important aspect is left unaddressed.

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?

Schema description coverage is 100%, with the window parameter fully explained in the schema (enum values and tradeoffs between windows). The description only briefly mentions the window in passing without adding any new semantics, 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 the tool returns trending data ('top tools, top packs, and total call volume') from 'other AI agents' calling on Pipeworx. It distinguishes from sibling tools like ask_pipeworx (question answering) and discover_tools (discovery) by focusing on aggregated call 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 'Useful for' section explicitly lists three concrete scenarios (discovering hot data sources, confirming canonical tool, checking alignment). It provides clear context but does not explicitly mention when not to use it or name alternative tools, keeping it slightly below the highest tier.

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