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 context: data source (CF analytics-engine), no PII, caching behavior (5min-1h depending on window), and self-aggregating nature. This goes well beyond the 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 approximately 100 words, front-loaded with the main output, followed by use cases, then technical details. Every sentence adds value, and there is no redundancy or filler.

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?

For a tool with a single optional parameter, no output schema, and complete annotation coverage, the description fully explains the return values (top tools, top packs, call volume), caching, and data sources. It is complete enough for an 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 only parameter (window) has 100% schema coverage with clear enum. The description explains the semantic difference: 'shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds significant meaning beyond the schema.

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 it 'returns the top tools, top packs, and total call volume' with specific verbs and resources. It distinguishes itself from siblings like 'discover_tools' or 'ask_pipeworx' by focusing on aggregate trending data.

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 (discovering hot data sources, confirming canonical choice, seeing alignment). While it does not list when not to use it, the use cases are concrete and helpful. No direct alternative tool is mentioned, but the purpose is unique enough.

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