US Population Data

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds context about the data source ('American Community Survey 5-year estimates'), coverage (all states, counties, sub-counties), and update frequency ('updated annually'), which is valuable beyond 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 description is concise with three focused sentences. It front-loads the core function, then adds scope, source, and attributes. No wasted words.

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 simple query tool with 3 parameters, 100% schema coverage, and an output schema, the description is adequately complete. It includes source, coverage, and data fields. It could mention the default year but the schema does that.

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% with detailed parameter descriptions. The description adds no additional semantic meaning about parameters, only that FIPS codes are used. Baseline 3 is appropriate as schema does the heavy lifting.

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 returns population counts for US geographies by FIPS code, specifying 'total, male, female'. It distinguishes itself from sibling tools like census.data.demographics and census.data.economic by focusing solely on population data.

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 implies its use for population data queries, but does not explicitly mention when to avoid it or provide alternatives. Given the sibling tools have names like 'demographics' and 'economic', the differentiation is clear, but no exclusion guidance is given.

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