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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, but the description adds valuable behavioral context: it is 'self-aggregating signal' derived from 'CF analytics-engine', contains 'no PII', and is 'cached 5min-1h depending on window'. These details inform the agent about data freshness and privacy implications beyond what annotations provide.

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 with the core purpose. It includes necessary details (use cases, data source, caching) in a compact form. Every sentence contributes value, and the list format for use cases is scannable. It is not overly verbose despite covering multiple aspects.

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?

The tool has no output schema, so the description carries the burden of explaining returns, which it does ('top tools, top packs, and total call volume'). It also covers window semantics, use cases, data origin, privacy, and caching. This is fully complete for a simple, read-only, one-parameter tool.

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 covers 100% of the parameter meaning (window enum with description explaining shorter vs longer windows). The tool description repeats the same window explanation but does not add further semantic detail. Since schema coverage is high, the baseline of 3 is appropriate; the description neither enhances nor detracts.

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 the tool's purpose with a specific verb ('Returns') and resource ('top tools, top packs, and total call volume over a recent window'). It explicitly targets 'What other AI agents are calling on Pipeworx right now', which differentiates it from sibling tools like discover_tools (which likely lists available tools, not usage trends). The purpose is unambiguous and distinct.

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 explicit use cases ('Useful for: (1) discovering what data sources are hot... (2) confirming a popular tool is the canonical choice... (3) seeing whether your use case aligns...'). This gives clear context for when to use the tool. However, it does not name alternatives or specify when-not-to-use, which would be needed for a 5.

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