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 context: self-aggregating signal derived from CF analytics-engine, no PII, and caching behavior (5min-1h depending on window). No contradiction with annotations.

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: core purpose first, then use cases, then technical details. Every sentence adds value with no redundancy. Appropriate length for the tool's complexity.

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 simple schema (1 optional param), rich annotations, and no output schema, the description covers all necessary aspects: outputs, derivation, caching, privacy. Complete for an agent to select and invoke 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?

Only one parameter (window) with 100% schema coverage and inline description. The tool description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand,' which provides decision-making context beyond the schema enum values.

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 the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d).' It uses specific verb 'returns' and lists three distinct outputs. Distinguishes from siblings like ask_pipeworx or discover_tools by focusing on aggregation rather than direct querying.

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 specific use cases are enumerated, providing clear context for when to use the tool. While it does not explicitly state when not to use or name alternatives, the use cases indirectly guide the agent away from inappropriate contexts (e.g., not for specific factual queries).

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