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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful context beyond annotations: data comes from daily aggregates, retention is 12 months, and dates are in the organization's time zone. It does not contradict 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?

Two sentences with no filler. The first sentence states the result and data source; the second gives the primary use case and the key date/time-zone qualifier. Every sentence earns its place.

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 read-only list tool with no output schema, the description explains the core output (frequent event names, counts, share) and the supporting data model. It could mention limit behavior or retention edge cases, but the schema and annotations cover the remaining operational details adequately.

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 description coverage is high at 80%, so the schema carries most parameter meaning. The description reinforces date-related semantics like calendar days and org-local time zone, but adds little for the limit parameter, which lacks a schema description.

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 the tool lists the organization's most frequent event names with counts and share over a date range. It also frames its role as a precursor to searching or computing stats, distinguishing it from siblings like search_events and event_stats.

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 gives clear usage context: use it to learn what an organization sends before searching or computing stats. It does not name sibling tools explicitly or provide explicit when-not-to-use conditions, but the intended workflow is clear.

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