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

Annotations already cover read-only and non-destructive behavior. The description adds useful context about the returned data (counts, earliest/latest dates, exact naming convention) and implies no side effects. This adds value beyond the annotations without contradicting them.

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 short sentences, front-loads the tool's purpose with a question-answer format, and then gives targeted usage guidance. No filler or redundancy.

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?

Given the presence of an output schema and descriptive annotations, the description sufficiently covers purpose, output content, and when to use it relative to a sibling. Nothing essential is missing for an agent to invoke it 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?

The schema fully describes the single optional parameter as a case-insensitive substring filter, achieving 100% coverage. The description does not add any further parameter-specific meaning, so it meets the baseline for high schema coverage.

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 agencies with upcoming rules, returning counts and dates, and explicitly contrasts it with the sibling list_rules_taking_effect by emphasizing exact agency naming. This is a precise verb-resource pair with scope and differentiation.

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 explicitly instructs when to call this tool: before filtering with list_rules_taking_effect, to obtain exact agency names. It names the alternative and provides a concrete use case (e.g., 'FAA' vs 'Federal Aviation Administration').

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