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

Annotations already declare readOnly and openWorld, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: a per-record credit cost with a minimum of 2, and the fact that results carry crawl-time availability rather than live data.

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?

Three front-loaded sentences with zero waste: what it returns and how it's ordered first, then the cost constraint, then the use case. 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?

With an output schema present, return values needn't be explained, and the description still usefully summarizes what results include. It covers ordering, cost, and use case; the only minor gap is no explicit contrast with the keyword-search sibling across a 10-parameter 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 coverage is 100%, so the baseline is 3. The description pushes above baseline by tying the credit cost to `page_size` and advising agents to keep it minimal, adding cost-aware semantics for a parameter the schema documents only structurally.

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?

Specific verb+resource+scope: 'Search DoorDash store records near a latitude and longitude, ordered by geographic distance with meters from the query point.' This clearly distinguishes it from the sibling datasets_doordash_stores_search (keyword-based) by its proximity ordering semantics.

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?

Provides a clear selection condition: 'Use this when location proximity matters.' This tells an agent when to pick this over the general search sibling, though it doesn't explicitly name or exclude the alternative tool to close the loop.

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