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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds valuable behavioral context: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This discloses data source, privacy, and caching behavior, which is useful for the agent to know the freshness and reliability of the data. This exceeds what annotations alone provide.

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 concise and well-structured: the first sentence states the core purpose, the second lists explicit use cases, and the third adds technical caveats. Every sentence earns its place, and there is no repetitive or redundant wording. It's appropriately sized for the tool's simplicity.

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 tool with one optional parameter and no output schema, this description is quite complete. It explains what the tool returns (top tools, packs, call volume), the data source and privacy (no PII), and caching variability. It does not give a full return structure, but it does specify the data components ('(pack, tool, count)'), which is sufficient for an agent to understand the output concept. Combined with the strong annotations, the description adequately covers the essential 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?

The input schema already fully describes the single 'window' parameter with enum values and a semantic explanation: '24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.' The tool description mentions the window options but adds no extra meaning beyond the schema. Since schema coverage is 100%, the baseline of 3 is appropriate.

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. Returns the top tools, top packs, and total call volume over a recent window.' This is specific with a verb ('Returns') and resource ('top tools, top packs, total call volume'), and it distinguishes itself from siblings like ask_pipeworx or query by focusing on aggregated agent usage trends rather than direct queries.

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 for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need.' These are clear contexts for when to use the tool. It doesn't explicitly mention alternatives or when not to use it, but the use cases effectively imply that it's for pre-query discovery and validation rather than direct information retrieval.

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