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

Annotations already declare readOnly/openWorld/idempotent/non-destructive. The description adds valuable behavioral context: it reveals the data source ('CF analytics-engine'), privacy ('no PII'), exact data shape ('(pack, tool, count)'), and caching ('Cached 5min-1h depending on window'). This goes well beyond the structured 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 moderately sized and well-structured: a leading definition, a list of outputs, a bulleted 'Useful for' section, and a final metadata note. It efficiently conveys important information, though there is slight redundancy between 'top tools, top packs, total call volume' and 'just (pack, tool, count)'.

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?

With no output schema, the description carries the burden of explaining return values. It covers the main data points (top tools, top packs, total call volume), time-window choices, caching behavior, and data shape. This is sufficient for a simple, read-only one-parameter tool, though it could mention the default window explicitly (left to the schema).

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 schema has 100% coverage of the single 'window' parameter, including enum values and a descriptive explanation of short vs long windows. The tool description itself does not add additional parameter semantics beyond what the schema already provides, so baseline score 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 opens with a clear statement of what the tool does: 'What other AI agents are calling on Pipeworx right now.' It precisely lists what is returned (top tools, top packs, total call volume) and distinguishes itself from sibling tools like discover_tools or recent_alerts by focusing on aggregate cross-agent usage trends.

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 enumerates three concrete use cases under 'Useful for:' (discovering hot data sources, confirming canonical tool choice, and checking alignment with aggregate demand). This provides clear when-to-use guidance, though it does not mention exclusions or explicitly name alternatives to avoid.

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