Census ACS Demographics

get_county_demographics

Get demographic data for counties: population, median age, race, Hispanic origin, income, and poverty.

Returns one record per county with total population, median age, racial breakdown
(White, Black, American Indian, Asian, Pacific Islander, Other, Two+),
Hispanic/Latino percentage, median household income, and poverty rate.

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). Data covers year-4 through year.

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

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the full transparency burden. It discloses that it returns one record per county, specifies the year range coverage, and explains parameter defaults (e.g., county_fips omitted returns all counties). It doesn't address error handling or auth, but for a read-only data retrieval tool, the core behavior is well covered.

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-structured with a clear purpose statement, a returns list, and an Args section. No sentence is wasted; the length is appropriate for the number of parameters and the data richness described.

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?

The description is highly complete for a data-retrieval tool. It covers return shape, parameter semantics, defaults, and behavior. The existence of an output schema further reduces the need to detail return values in the description, but the description already does so thoroughly.

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 description adds significant meaning beyond the schema's type-only definitions. It explains valid formats ('WA', 'CA', or 2-digit FIPS), the exact length of county_fips ('033'), the omission behavior for county_fips, and the time range implied by year ('data covers year-4 through year'). This fully compensates for the 0% schema description coverage.

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 uses a specific verb ('Get') and resource ('demographic data for counties'), then enumerates the exact data fields returned. This clearly distinguishes it from sibling tools like get_county_economics or get_commuting_data.

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

Usage Guidelines4/5

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

The description clearly communicates the tool's context by specifying the demographic focus and the return fields. It doesn't explicitly name alternatives or say when not to use it, but the scope is evident enough for an agent to select it appropriately.

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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