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

Annotations already declare read-only and idempotent behavior. The description adds valuable context: data derives from CF analytics-engine, no PII is included, the output is (pack, tool, count), and caching depends on the window (5min-1h). No contradiction 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 well-structured: a concise purpose statement, a numbered 'useful for' list, and a final note on data provenance and caching. Each sentence adds value without 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 low-complexity tool with one optional parameter and no output schema, the description thoroughly covers the output shape (pack, tool, count), the available time windows, and the use cases. It gives an agent enough to select and invoke the tool correctly.

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% and the schema already describes the window parameter with enum values and meaning. The description repeats the options and adds a caching hint, but that is more behavioral than semantic. Baseline 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 it returns top tools, top packs, and total call volume over a selectable window. The opening phrase 'What other AI agents are calling on Pipeworx right now' gives a specific, distinct purpose that differentiates it from sibling tools like discover_tools.

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?

It provides three explicit use cases: discovering hot data sources, confirming a canonical tool, and checking alignment with agent needs. This gives clear when-to-use context, but it does not explicitly mention alternatives or when not to use the tool.

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