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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it explains the data source (CF analytics-engine), privacy (no PII), data structure (pack, tool, count), and caching policy (5min-1h). This goes well beyond the annotations and provides critical operational insight.

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 (four sentences) and front-loaded with the main function. Every sentence adds value: purpose, use cases, data origin, and caching. No redundant information.

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's simplicity (one optional parameter, no output schema), the description covers the core return values (top tools, top packs, total call volume) and the caching behavior. It does not specify the exact output format, but the information provided is sufficient for safe invocation.

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 has 100% coverage with a descriptive enum for the 'window' parameter. The description adds extra semantics by explaining the trade-off between shorter and longer windows ('hot right now' vs 'steady-state demand'), which aids the agent in choosing the appropriate value.

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 uses a specific verb ('Returns') and clearly identifies the resources ('top tools, top packs, and total call volume') over configurable windows. It distinctly distinguishes its aggregation purpose from sibling tools like 'ask_pipeworx' or 'discover_tools' which handle direct queries or tool discovery.

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 concrete use cases (discovering hot data sources, confirming canonical tool, aligning use case). While it does not specify when not to use or provide direct alternatives, the context is clear and sufficient for an AI agent to determine applicability.

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