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

Annotations already provide readOnlyHint=true and openWorldHint=false, so the description adds context beyond those: it discloses that datasets have supported vintage years and that each entry names required predicates and geography levels, which vary by dataset. This tells the agent what kind of information the tool returns without contradicting the 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 four-sentence description is efficient: purpose, when-to-use, downstream output consumption, and the nature of each returned entry. The enumeration of dataset types is slightly listy but adds useful context. The core purpose is front-loaded in the first sentence.

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 list tool with one optional parameter, no required parameters, a read-only annotation, and an output schema, the description is complete. It covers what the tool does, when to use it, what the returned entries contain, and how to use the dataset_id afterward. No critical information is missing.

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?

There is only one parameter, filter, and the schema already fully describes it ('Keyword to filter datasets by name or description. Omit to list all datasets.'), giving 100% schema coverage. The description adds output-related context (dataset_id reuse) but not additional input semantics, so the baseline 3 applies.

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 'Browse available Census Bureau datasets', a clear verb+resource pair, and explicitly names the dataset families covered (ACS5, ACS1, population estimates, decennial census, business datasets). It frames itself as the discovery/starting point, distinguishing it from sibling list tools like census_list_geographies and census_list_predicate_values.

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 an explicit selection condition: 'Use as the starting point when the right dataset is unknown.' It also explains what each dataset description contains (required predicates, geography levels) so an agent can judge fit, and notes that the resulting dataset_id is fed into other census tools. It does not explicitly list exclusions, but the starting-point framing strongly implies when not to use it.

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