Census ACS Demographics

get_tract_data

Get tract-level ACS data for any variables within a county.

This is a flexible tool for querying any ACS 5-year estimate variables at
the census tract level. Automatically batches requests if more than 50
variables are requested.

Common variable examples:
- B01001_001E: Total population
- B19013_001E: Median household income
- B17001_002E: Population below poverty level
- B25077_001E: Median home value
- B02001_002E-008E: Race breakdown

Args:
    state: Two-letter state abbreviation (e.g. 'WA') or 2-digit FIPS code.
    county_fips: Three-digit county FIPS code (e.g. '033' for King County, WA).
    variables: Comma-separated ACS variable codes (e.g. 'B01001_001E,B19013_001E').
               NAME is always included automatically.
    year: ACS 5-year estimate year (default 2022).

Input Schema

TableJSON Schema

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

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 are present, so the description carries the burden of disclosing behavior. It adds valuable details: automatic batching for more than 50 variables, automatic inclusion of the NAME variable, and the default year. These behaviors are not evident from the schema. However, it does not disclose potential errors, rate limits, or output structure, which prevents a perfect score.

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

Conciseness4/5

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

The description is well-organized: a one-sentence summary, a note on flexibility, common variable examples, and a clear Args block. Every section serves a purpose, and the length is appropriate for the tool's complexity. It could be slightly more concise by trimming variable examples, but they add practical value.

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

Completeness4/5

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

Given the tool has four parameters, no annotations, and an output schema (which likely documents return values), the description is complete enough. It covers purpose, parameter semantics, and a key behavior (batching). It does not discuss restrictions on year range or possible errors, but these are not critical for a data-query tool.

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 provides only property names and types with no descriptions. The description fully compensates by explaining each parameter's format and giving concrete examples: state as 'WA' or FIPS, county_fips as a 3-digit code, variables as comma-separated codes, and year with a default. This is far beyond the schema's minimal definitions.

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 opens with 'Get tract-level ACS data for any variables within a county,' clearly stating the action (get), resource (tract-level ACS data), and scope (within a county). It distinguishes itself from sibling tools like get_county_demographics by emphasizing tract-level granularity and arbitrary variable selection.

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?

It describes itself as a 'flexible tool for querying any ACS 5-year estimate variables at the census tract level,' giving clear context for when to use it: when you need tract-level ACS data with custom variables. It does not explicitly name alternatives or exclusions, but the context is strong enough to infer appropriate usage.

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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