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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavior context: it discloses the data source (CF analytics-engine), privacy (no PII), output structure (pack, tool, count), and caching behavior (5min-1h depending on window). No contradictions.

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: front-loaded with the core output, then a numbered list of use cases, then extra context about data source and caching. Every sentence contributes value and there is zero redundancy or 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 simple tool with one optional parameter and no output schema, the description fully covers what the agent will get (top tools, top packs, call volume), the window options, and important caveats (caching, no PII). It is complete 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?

Schema coverage is 100% with only one parameter ('window'), and the schema description already explains the enum options and the trade-off between hot and steady-state signals. The tool description repeats this info but adds no new semantics, so the baseline score 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 states a specific verb ('Returns') and resource ('top tools, top packs, and total call volume') with a precise scope: what other AI agents are calling on Pipeworx. It clearly distinguishes from siblings like get_top_articles or discover_tools by focusing on agent call trends, not general articles or tool 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 three explicit use cases with numbers, giving clear context on when to use the tool. It does not explicitly name alternative tools or state when not to use it, but the use cases are specific enough to guide selection among the large sibling list.

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