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

Beyond the annotations (readOnly, idempotent), the description adds valuable context: it's a self-aggregating signal derived from CF analytics-engine, contains no PII, returns only (pack, tool, count) tuples, and is cached 5min-1h depending on window. This enriches the safety and freshness profile.

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 front-loaded. The opening sentence immediately explains what the tool returns, followed by a clear list of use cases and two succinct sentences on data source, privacy, and caching. Every sentence earns its place with no fluff.

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 single-optional-parameter, read-only tool with no output schema, the description is complete. It covers return values (top tools, top packs, total call volume), parameter semantics, use cases, caching behavior, and data provenance. Nothing significant is missing.

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 baseline is 3. The description adds interpretive meaning for the 'window' parameter: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This goes beyond the enum literal descriptions, providing guidance on choosing between 24h, 7d, and 30d.

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 with a specific verb and resource: 'Returns the top tools, top packs, and total call volume over a recent window.' It distinguishes itself from siblings like discover_tools by focusing on aggregate trending signals from other AI 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?

Provides explicit use cases in a numbered list: discovering hot data sources, confirming canonical tool choice, and checking alignment with agent needs. It doesn't mention when-not-to-use or alternative tools, but the 'Useful for' section gives clear context for when to invoke this tool.

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