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

Beyond the readOnly/idempotent annotations, the description discloses the data source ('derived from CF analytics-engine'), privacy guarantees ('no PII'), the exact data shape ('just (pack, tool, count)'), and caching behavior ('Cached 5min-1h depending on window'). This provides substantial behavioral context not available in 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 well-structured, front-loaded with the primary purpose, and every sentence earns its place. The bullet-style 'Useful for' list is compact, and the final sentence adds important transparency without redundancy.

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 single-parameter, read-only aggregation tool with no output schema, this description is comprehensive. It explains what is returned (top tools, packs, call volume), the time windows, the data provenance, caching, and the absence of PII, leaving no critical gaps.

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 already fully describes the single 'window' parameter with an enum and explanation of short vs. long windows. The tool description mentions the windows but adds no new semantic meaning beyond the schema, so it meets the baseline of 3 for high schema coverage.

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: 'What other AI agents are calling on Pipeworx right now' and specifies the output ('top tools, top packs, and total call volume'). It distinguishes itself from siblings like discover_tools by focusing on real-time aggregate usage trends rather than listing available tools.

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 'Useful for' section provides three explicit use cases: discovering hot data sources, confirming canonical tool choice, and checking alignment with other agents' needs. It gives clear context for when to use the tool, though it does not explicitly name alternatives or state when not to use it.

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