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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description reveals the tool's underlying data source (CF analytics-engine), privacy guarantees (no PII), and caching behavior (cached 5min-1h depending on window). This is valuable behavioral context that helps the agent set expectations about freshness and data provenance. The description does not contradict any annotation.

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 three sentences with a numbered list, making it well-structured and scannable. Every sentence earns its place: the first defines the output, the second lists use cases, and the third explains data provenance, privacy, and caching. It is appropriately sized for the tool's simplicity and richly informative without being verbose.

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?

Despite having no output schema, the description clearly indicates what the return contains ('top tools, top packs, total call volume', 'just (pack, tool, count)'). It also covers latency (cached) and the data source, giving the agent a complete mental model for a tool with a single optional parameter. The level of detail is appropriate for the tool's complexity.

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 defines the enum for window, but the description adds crucial semantic guidance: shorter windows for current trends, longer windows for steady-state demand. This goes beyond the schema's bare enum description and helps the agent choose the right value. The single parameter is fully explained, and the description also notes the default behavior (24h default in schema) indirectly.

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 returns top tools, top packs, and total call volume over a recent window. It uses a specific verb ('Returns') and resource ('top tools, top packs, and total call volume'), distinguishing it from sibling tools like discover_tools. The opening line 'What other AI agents are calling on Pipeworx right now' succinctly conveys the unique value proposition.

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 explicitly lists three concrete use cases (discovering hot data sources, confirming canonical choice, aligning with agent needs), which fully answers 'when to use this tool.' It also provides guidance on choosing the window parameter ('Shorter windows surface what's hot right now; longer windows show steady-state demand'), effectively explaining the tradeoffs. No explicit exclusions are given, but the use cases imply clear boundaries.

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