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 beyond these: it states the data source ('derived from CF analytics-engine'), privacy ('no PII'), and cache freshness ('Cached 5min-1h depending on window'). This extra behavioral transparency about data provenance and staleness earns a 4.

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 well-structured and front-loaded: it starts with a concise summary, states the return values, lists three use cases in a bulleted format, and closes with a note on data source and caching. Every sentence adds value with no redundancy, making it appropriately sized for the amount of information conveyed.

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 read-only analytics tool with one optional parameter and no output schema, the description covers all essential aspects: what it returns (top tools, top packs, count), the meaning of the window parameter, concrete use cases, data source, privacy, and cache behavior. The content is sufficient for an agent to select and invoke the tool correctly without additional information.

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 single parameter 'window' is fully documented in the schema with enum values and an explanatory description ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). Schema coverage is 100%, so the baseline is 3. The tool description adds no additional parameter-level meaning beyond what the schema already provides.

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 opens with a clear question ('What other AI agents are calling on Pipeworx right now') and explicitly states the return items: 'top tools, top packs, and total call volume over a recent window.' This specific verb+resource+scope framing distinguishes it from sibling tools like discover_tools, which is about finding tools rather than seeing real-time aggregate usage.

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 provides three explicit use cases ('discovering what data sources are hot for current events,' 'confirming a popular tool is the canonical choice before asking your own question,' and 'seeing whether your use case aligns with what most agents need'). This gives clear context for when to use it, but it does not mention when NOT to use it or name alternative tools, so it falls 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.