HMDA Mortgage Data

get_lending_by_race

Get mortgage lending patterns disaggregated by race and ethnicity.

Returns mortgage origination and denial data broken down by the
race/ethnicity of the applicant. Useful for fair lending analysis
and identifying disparities in mortgage access.

Args:
    state: Two-letter US state abbreviation (e.g. 'CA', 'TX').
    county_fips: Five-digit county FIPS code (e.g. '06037' for LA County).
    year: Data year (e.g. 2022). Defaults to 2022 if not specified.

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.9/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions that it returns origination and denial data and that year defaults to 2022, but it omits critical details such as data source, geographic aggregation level, permission requirements, or what happens when only part of the arguments are supplied. This is a significant transparency gap.

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 concise and well-structured: a front-loaded purpose sentence, a brief return/use-case sentence, and a neatly formatted Args section. Every sentence earns its place with no redundancy or filler.

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

Completeness3/5

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

The presence of an output schema reduces the need to explain return values. However, the tool has three optional parameters with no stated interactions or minimum required combination, and there are no annotations for safety or side effects. The description covers the basics but leaves gaps about data scope and partial-argument behavior.

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

Parameters4/5

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

The schema provides no parameter descriptions (0% coverage), but the description adds meaningful context for each parameter: state uses two-letter abbreviation examples, county_fips uses a five-digit example, and year includes a default. This compensates well, though the relationship between state and county_fips is not explained.

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 mortgage lending patterns disaggregated by race and ethnicity' — a specific verb and resource with a clear modifier. This distinguishes it from siblings like get_aggregate_stats (aggregate) and get_denial_reasons (denial-focused), leaving no ambiguity about its purpose.

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 explicitly states 'Useful for fair lending analysis and identifying disparities in mortgage access,' providing a clear context for when to use this tool. It doesn't explicitly name alternative tools or exclusions, but the use case is sufficiently distinct from siblings.

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

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TDQS

A4.1/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: get_aggregate_stats provides summary statistics, get_denial_reasons focuses on denial breakdowns, get_lending_by_race analyzes racial disparities, and get_mortgage_data returns detailed loan-level records. The descriptions clearly differentiate their specific analytical functions within the HMDA domain.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with 'get_' prefix followed by descriptive nouns (aggregate_stats, denial_reasons, lending_by_race, mortgage_data). The naming convention is perfectly uniform throughout the toolset, making them predictable and easy to understand.

Tool Count4/5

Four tools is reasonable for a mortgage data analysis server, covering key analytical perspectives (aggregate statistics, denial analysis, racial disparities, and detailed data). However, the scope feels slightly thin - additional tools for filtering by lender, property type, or income brackets could enhance completeness without being excessive.

Completeness4/5

The toolset covers the core analytical needs for HMDA data exploration with different granularities and perspectives. Minor gaps exist: there's no tool specifically for lender-level analysis, no ability to compare multiple geographies simultaneously, and no specialized tools for time-series analysis across years. However, agents can work around these limitations using the existing tools effectively.

Resources

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