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

Annotations already declare readOnlyHint=true, and the description adds meaningful behavioral context beyond that: dataset descriptions name required predicates and geography levels, both of which vary by dataset. It also notes that each dataset supports vintage years, which informs the agent about what the listing contains. No contradiction with 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 compact and every sentence earns its place: scope, usage context, downstream integration, and dataset-specific behavioral notes. The key purpose is front-loaded, and no redundant or filler content appears.

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 one optional parameter, strong annotations, and an output schema present, the description fully covers what an agent needs to select and invoke this tool. It explains when to use it, what the results contain, how results vary, and how to use the returned dataset_id elsewhere.

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 input schema has 100% coverage for its single optional filter parameter, describing it as a keyword to filter by name or description. The description does not add parameter-specific detail, but that is acceptable because the schema fully documents the parameter. Baseline 3 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 specific verb and resource: 'Browse available Census Bureau datasets with their supported vintage years.' It clearly differentiates this from sibling tools by framing it as the starting point when the right dataset is unknown, and by explaining how the results feed into other census tools via dataset_id.

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 states when to use it: 'Use as the starting point when the right dataset is unknown.' It also explains that the dataset_id returned should be passed to the dataset parameter in other census tools, giving clear downstream context. It does not name sibling tools as alternatives or state explicit exclusions, but the guidance is strong enough for an agent to select this tool appropriately.

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