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

Beyond the annotations (readOnly, openWorld, idempotent), the description reveals that the data is self-aggregating, sourced from CF analytics-engine, contains no PII, and is cached for 5min-1h depending on the window. This adds valuable behavioral context beyond what annotations provide.

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 and well-structured: a front-loaded main statement, followed by bulleted use cases, and then implementation details. Every sentence adds value without redundancy.

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 the tool has one optional parameter, no output schema, and comprehensive annotations, the description covers all necessary aspects: what it returns, how to use it, caching behavior, and data sourcing. No gaps remain.

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 coverage is 100% with enum descriptions. The description adds interpretive context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enhances the schema's meaning, justifying a score above baseline 3.

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 specifies the verb 'returns' and the resources 'top tools, top packs, and total call volume' over a configurable time window. It distinguishes this analytics tool from sibling tools like 'ask_pipeworx' or 'discover_tools' by focusing on aggregated trending data rather than querying or searching.

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?

Three explicit use cases are provided, explaining when this tool is valuable (e.g., discovering hot data sources, confirming canonical tools). It also explains the window parameter's behavior (short vs. long windows). It does not explicitly state when not to use it, but the guidance is clear enough for an agent to decide.

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