Census ACS Demographics

get_county_economics

Get economic data for counties: income, poverty, home values, rent, and health insurance.

Returns median household income, poverty rate, median home value, median gross rent,
and health insurance coverage rates (insured vs uninsured).

Args:
    state: Two-letter state abbreviation (e.g. 'WA', 'CA') or 2-digit FIPS code.
    county_fips: Three-digit county FIPS code (e.g. '033' for King County).
                 Omit to get all counties in the state.
    year: ACS 5-year estimate year (default 2022).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`state`	Yes
`county_fips`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior4/5

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

No annotations exist, so the description carries the full burden. It discloses the data source (ACS 5-year estimates), what metrics are returned, and the effect of omitting county_fips. This is transparent for a read-only data retrieval tool, though it does not discuss rate limits or auth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with a brief summary, a returns list, and clear parameter documentation. Every sentence adds useful information without redundancy, making it easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, the presence of an output schema, and absence of annotations, the description is complete. It covers purpose, return metrics, parameter specifics, data source, and defaults, enabling correct selection and invocation. No critical context is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% coverage, but the description thoroughly explains all three parameters: state with examples ('WA', 'CA' or FIPS), county_fips with format and omission behavior, and year with default and meaning. This fully compensates for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets economic data for counties and enumerates specific metrics (income, poverty, home values, rent, health insurance). This verb+resource+scope structure distinguishes it from sibling tools focusing on commuting, demographics, education, and tract data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage details like omitting county_fips for all counties and the default year, but it does not explicitly explain when to choose this tool over sibling tools such as get_county_demographics or get_county_education. Usage context is implied by the listed metrics but no alternatives are mentioned.

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

All Census ACS Demographics tools Inspect

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct subject area (commuting, demographics, economics, education) or geographic level (tract). No two tools overlap in purpose, making selection unambiguous.

Naming Consistency4/5

All tools start with 'get_' and mostly follow a 'get_county_<topic>' pattern, but 'get_commuting_data' and 'get_tract_data' deviate slightly from the fully consistent pattern. Still, the convention is predictable and readable.

Tool Count5/5

Five tools is well-scoped for a Census ACS demographics server. Each thematic area and geographic level earns its place without unnecessary bloat or redundancy.

Completeness4/5

The four county-level tools cover key demographic, economic, education, and commuting themes, and the flexible tract-level tool can query any ACS variable, covering niche needs. A minor gap is the lack of a dedicated county-level variable query, but the tract tool compensates.

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