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get_denial_reasons

Get mortgage denial reasons breakdown for a geographic area.

Returns the distribution of denial reasons for mortgage applications
that were denied. Useful for identifying barriers to homeownership
in specific communities.

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
NameRequiredDescriptionDefault
yearNo
stateNo
county_fipsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It clearly states that the tool returns a distribution of denial reasons, implying a read-only operation. It does not mention any side effects, permissions, or error behaviors, but for a 'Get' tool this is adequate. However, it omits details about parameter combinations (e.g., whether state and county_fips are both required), which could affect behavior.

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: three short sentences plus an Args list. The first sentence states the core function, the second explains the output, and the third provides context. Every sentence has clear value with no padding.

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?

The description covers the tool's purpose, output type, use case, and all parameters with examples. An output schema exists, so return value details are not needed. However, it does not clarify whether state and county_fips are individually sufficient or must be used together, nor does it explain the implications of leaving all parameters optional. This is a minor but notable gap.

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?

Schema description coverage is 0%, but the description's Args section fully compensates. Each parameter is explained with format and examples: state is 'two-letter US state abbreviation', county_fips is 'five-digit county FIPS code (e.g. 06037)', and year is 'data year (e.g. 2022)' with a default. This is exemplary parameter documentation.

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's purpose: 'Get mortgage denial reasons breakdown for a geographic area.' This specifies a concrete action ('Get') and resource ('mortgage denial reasons breakdown') with a geographic scope, which distinguishes it from sibling tools like get_aggregate_stats or get_lending_by_race.

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 provides a clear use case: 'Useful for identifying barriers to homeownership in specific communities.' This gives context for when to apply the tool, though it does not explicitly contrast with sibling tools or state when not to use it. The guidance is sufficient but lacks direct exclusions or alternative recommendations.

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