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

With readOnlyHint and idempotentHint already true, the description adds valuable context: it explains the data source (CF analytics-engine), states no PII is involved, describes the output structure as '(pack, tool, count)', and discloses caching behavior (5min-1h). This goes beyond the annotations and provides behavioral transparency.

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 three sentences, front-loaded with the main purpose, followed by concrete use cases and technical details. Every sentence adds value, and the structure is easy to scan.

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?

Despite no output schema, the description explains what is returned (top tools, top packs, total call volume) and the data shape ('(pack, tool, count)'). It also covers caching and data provenance. For a simple read-only tool with one optional parameter, this is complete.

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 already covers the single parameter 'window' with a description that includes defaults and semantics (shorter vs longer windows). The tool description merely mentions the window options without adding new meaning, so the baseline of 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 clearly states what the tool does: returns top tools, top packs, and total call volume for what other AI agents are calling on Pipeworx. It uses specific verbs ('returns') and names the resource ('Pipeworx trending'), and the phrasing distinguishes it from siblings like discover_tools by focusing on current agent 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 explicit 'Useful for' use cases: discovering hot data sources, confirming canonical tools, and checking use-case alignment. These are clear when-to-use contexts, but it does not explicitly mention when not to use or name alternative tools, so it falls short of the full 5.

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