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

Beyond the annotations (readOnlyHint, idempotentHint), the description adds valuable behavioral context: it is a 'self-aggregating signal derived from CF analytics-engine', guarantees 'no PII', and discloses caching behavior ('Cached 5min-1h depending on window'). It also clarifies the output structure as '(pack, tool, count)', which is useful given no output schema.

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: the main purpose is stated in the first sentence, followed by concrete return values, use cases, and then behavior/data provenance. Every sentence earns its place, with no redundancy or fluff. The numbered use-case list is compact and easy to scan.

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 tool with one optional parameter and no output schema, the description is comprehensive. It explains what the tool returns, why it's useful, how it derives data, privacy guarantees, and caching behavior. No critical context is missing for an agent to decide whether and how to invoke it.

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 already covers the window parameter at 100% with an enum and description, but the tool description adds extra meaning by linking the window to caching time ('Cached 5min-1h depending on window') and reinforcing the semantic distinction between shorter and longer windows. This goes beyond the schema's generic description.

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 function: 'What other AI agents are calling on Pipeworx right now' and specifies the outputs: 'top tools, top packs, and total call volume'. It distinguishes itself from siblings like discover_tools by focusing on aggregated usage trends rather than tool discovery, making it a specific verb+resource+scope definition.

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: 'discovering what data sources are hot for current events', 'confirming a popular tool is the canonical choice', and 'seeing whether your use case aligns with what most agents need'. While it does not name alternative tools or state when not to use it, the contextual guidance is clear and actionable.

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