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

Annotations declare readOnlyHint, idempotentHint, destructiveHint=false. Description adds value by noting daily data refresh and specifying exact return fields, exceeding what annotations provide. No contradiction.

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?

Single paragraph, no wasted words. Starts with purpose, lists returned fields, gives concrete examples, and ends with scope. Efficient and front-loaded.

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?

Describes all return details (since no output schema) and data freshness. Does not explicitly state default behavior when no parameters given (returns first 50 offices?), but limit param covers that. Adequate for a lookup tool.

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 covers all four parameters with descriptions. The description adds context with examples like 'Columbia' for city and '63101' for ZIP, and explains usage patterns (substring matching, prefix). This enhances understanding beyond schema alone.

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?

Description clearly states 'Find a Missouri license office' and enumerates returned fields (address, phone, hours, coordinates, etc.). Provides example queries that differentiate from sibling tools (e.g., 'DMV office in Springfield MO' vs. unrelated tools like deep_research).

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?

Examples of use cases (closing dates, office hours, location queries) implicitly guide when to use. No explicit exclusions or alternatives, but context of sibling tools makes it clear this is the DMV office tool.

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