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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses important behavioral details: data source ('derived from CF analytics-engine'), privacy ('no PII'), result shape ('just (pack, tool, count)'), and caching ('Cached 5min-1h depending on window'). This adds significant transparency.

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 compact yet information-dense, using three sentences to cover purpose, use cases, and technical details. The colon-separated list is easy to parse, and no sentence is wasted.

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 one-parameter tool with no output schema, the description covers everything: what it returns, when to use it, data source, caching, and privacy. It gives a complete mental model without requiring additional context.

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?

Schema coverage is 100% and the enum values are already described in the schema. The tool description adds further semantic guidance by explaining trade-offs: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This helps agents choose the right window.

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 statement of what the tool does: 'What other AI agents are calling on Pipeworx right now' and lists the exact outputs (top tools, top packs, total call volume). It distinguishes itself from sibling tools like discover_tools by focusing on real-time aggregated usage trends rather than 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 provides a 'Useful for' section with three explicit use cases, giving clear context for when to use the tool. It does not name alternative tools or exclusion conditions, but the use cases are specific enough to guide selection.

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