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 valuable behavioral context: it explains the data source (CF analytics-engine), confirms no PII, specifies the returned tuple structure (pack, tool, count), and mentions caching behavior (5min-1h depending on window). This goes well beyond the annotations and gives the agent a complete picture of what to expect.

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 and front-loaded: it opens with a concise statement of what it returns, then lists concrete use cases, and ends with technical details on data provenance and caching. No sentence is wasted; the length is justified by the useful information it conveys.

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?

With no output schema, the description adequately explains the return values (top tools, top packs, total call volume) and even gives the data shape (pack, tool, count). It also covers caching, privacy, and parameter behavior, making the tool fully understandable for selection and invocation. The inclusion of use cases adds contextual richness.

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 the single 'window' parameter with a full description including defaults and tradeoffs ('24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.'). The tool description repeats this information without adding new semantics, so with 100% schema coverage, the baseline score 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 trending data on what other AI agents are calling on Pipeworx, specifically top tools, top packs, and total call volume over a window. It uses a specific verb ('returns') and resource ('trending agent usage'), and distinguishes itself from sibling tools by focusing on self-aggregating signal from CF analytics-engine.

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 explicitly lists three use cases under 'Useful for,' which tell the agent when to use this tool (discovering hot data sources, confirming canonical choices, checking alignment with agent demand). It does not explicitly mention alternatives or when not to use it, but the provided contexts are clear enough for a simple read-only tool.

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