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

Beyond annotations that already declare readOnlyHint and idempotentHint, the description adds valuable context: the data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h). This fully informs the agent of the tool's behavior.

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 concise (4 sentences) and well-structured: first sentence states what it returns, then bullet-style use cases, followed by technical details. Every sentence adds value with no fluff.

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 simple tool with one parameter and no output schema, the description covers everything needed: return fields, window options, caching, and data source. It is complete for an AI agent to use correctly.

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 single parameter 'window' has 100% schema coverage with an enum. The description adds semantic meaning by explaining the trade-off: shorter windows for hot trends, longer windows for steady-state demand. This goes beyond the schema's enum labels.

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 returns: top tools, top packs, and total call volume over a recent window. The verb 'returns' is specific, and the resource (trending data on Pipeworx) is well-defined. It distinguishes itself from sibling tools like 'ask_pipeworx' or 'discover_tools' by focusing on usage statistics.

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 lists three explicit use cases (e.g., discovering hot data sources, confirming popular tools, aligning use case with agent needs). It does not explicitly state when not to use the tool or suggest alternatives, but the context is clear enough for an AI agent to decide.

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