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

Annotations already declare readOnly and idempotent hints, but the description adds substantial context: data source (CF analytics-engine), no PII, caching (5min-1h), and the self-aggregating nature. This goes beyond the annotation metadata and helps the agent understand response characteristics and limitations.

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 lead sentence stating the purpose, a list of use cases, and a compact note on data provenance/caching. Every sentence adds information without repeating schema or annotations.

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 simple single-parameter read-only tool, the description covers output (top tools, packs, counts), usage context, caching behavior, privacy, and data source. This is complete enough for an agent to decide when to call the tool and what to expect in the response, even without an output schema.

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 schema fully documents the window parameter with an enum and description (100% coverage). The description adds interpretive value by explaining that shorter windows surface hot trends while longer windows show steady-state demand, which is beyond the schema's literal definition.

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: it returns trending tools, packs, and call volume on Pipeworx. This distinguishes it from query/research tools like ask_pipeworx or discover_tools, making the purpose unambiguous.

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 (hot data sources, canonical tool confirmation, alignment with agent needs) and explains window trade-offs. However, it does not explicitly mention when not to use the tool or name alternative tools, so it falls just short of a 5.

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