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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral details: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This clarifies data provenance, privacy, and freshness beyond what annotations offer.

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 (5 lines) with well-structured bullet points for use cases. Every sentence adds value, no fluff. It front-loads the core functionality and then provides context.

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 1 optional parameter and no output schema, the description is remarkably complete. It covers purpose, parameters, use cases, data source, caching behavior, and privacy (no PII). No gaps remain for the agent to make informed decisions.

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?

Schema coverage is 100%, so the baseline is 3. The description adds semantic nuance for the 'window' parameter: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This provides actionable guidance on choosing window values.

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: 'Returns the top tools, top packs, and total call volume over a recent window.' It uses a specific verb ('Returns') and resource ('trending data on Pipeworx'), and distinguishes itself from sibling tools like ask_pipeworx or discover_tools by focusing on aggregate usage data across agents.

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 (e.g., discovering hot data sources, confirming canonical choices, aligning use cases). It provides context for when to use each window. However, it does not explicitly state when not to use the tool or mention alternatives, though the unique nature of the tool makes this less critical.

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