NCES Education Data

get_school_demographics

Get school enrollment broken down by race/ethnicity.

Returns enrollment counts by demographic group from the CCD,
including White, Black, Hispanic, Asian, Native American,
Pacific Islander, Two or More Races, and total enrollment.

Args:
    state: Two-letter US state abbreviation (e.g. 'CA', 'NY').
    county_fips: Optional 5-digit county FIPS code to filter by county.
    year: School year to query (default 2022).
    limit: Maximum number of schools to return (default 50, max 500).

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.2/5.0

Behavior3/5

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

There are no annotations, so the description carries the burden. It discloses the source (CCD), the demographic groups included, and total enrollment, plus default year and limit. However, it does not mention data suppression, privacy thresholds, or what happens with missing data, leaving some behavioral aspects undisclosed.

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-structured with a brief summary and an Args section. It lists all demographic groups, which is slightly verbose but adds value. Overall, it is appropriately sized and front-loaded with the core purpose.

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 that an output schema exists, the description does not need to detail return values, and it doesn't. It covers the main inputs and the nature of the output (enrollment counts by group). It could add a note on ordering or aggregation, but is complete enough for a demographics lookup 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 has 0% description coverage, so the description fully compensates by explaining each parameter: state (two-letter abbreviation), county_fips (optional 5-digit filter), year (default 2022), and limit (default 50, max 500). This adds crucial meaning beyond the bare schema types and defaults.

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 'get school enrollment broken down by race/ethnicity' using a specific verb and resource. It distinguishes itself from sibling tools (e.g., get_graduation_rates, get_district_finance) by focusing on demographic enrollment 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 gives clear context on what data it returns (enrollment counts by demographic group from CCD), which implies when to use it over siblings. It does not explicitly state alternatives or exclusions, but the scope is unambiguous.

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

All NCES Education Data tools Inspect

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct data type: finance, directory overview, graduation rates, demographics, and school directory. Although some tools overlap in attributes (e.g., enrollment in get_schools and get_school_demographics), the level of detail and purpose are clearly separated.

Naming Consistency5/5

All tool names follow a consistent 'get_' prefix and snake_case convention, with predictable patterns like get_district_finance and get_school_demographics. This makes the set easy to navigate.

Tool Count5/5

The server has 5 tools, which is well within the ideal range for a focused data retrieval domain. Each tool covers a meaningful aspect of NCES education data without being redundant or overwhelming.

Completeness4/5

The set covers key K-12 data categories: finance, demographics, graduation, and directory information. Minor gaps (e.g., district-level demographics, state-level aggregates, or test scores) exist, but the core data types are well represented and there are no dead ends.

Resources

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