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

Annotations already declare readOnly/idempotent/non-destructive. The description adds valuable behavioral context: 'derived from CF analytics-engine, no PII, just (pack, tool, count)' and 'Cached 5min-1h depending on window,' informing users about data freshness and privacy. No contradictions with 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 front-loaded with the core purpose, followed by structured bullet points for use cases and a final sentence for caching/PII. Each sentence contributes value, but the length could be trimmed without losing key information, so not a 5.

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?

With no output schema, the description sufficiently explains return values ('top tools, top packs, and total call volume') and also gives additional context like data source, no PII, and caching behavior. For a single-parameter, read-only tool, this is complete.

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 only parameter 'window' is fully described in the schema (enum, default, effect). The description repeats the window options and semantics ('Shorter windows surface what's hot right now') but adds no net new meaning, so baseline 3 applies.

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, specific verb and resource: 'What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume.' It distinguishes itself from siblings like 'discover_tools' by focusing on real-time aggregated call activity rather than general 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 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...'). It lacks explicit when-not-to-use or named alternatives, but the context 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.