HMDA Mortgage Data

get_mortgage_data

Get Home Mortgage Disclosure Act (HMDA) loan-level data.

Returns mortgage application and origination records reported by
financial institutions under HMDA. At least one geographic filter
(state or county_fips) is recommended to limit results.

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.
    action_taken: Loan action code: '1' (originated), '2' (approved not
        accepted), '3' (denied), '4' (withdrawn), '5' (incomplete).
    loan_type: Loan type code: '1' (conventional), '2' (FHA-insured),
        '3' (VA-guaranteed), '4' (USDA/RHS).
    limit: Maximum number of records to return (default 100, max 1000).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`limit`	No
`state`	No
`loan_type`	No
`county_fips`	No
`action_taken`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.3/5.0

Behavior3/5

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

No annotations are provided, placing full burden on the description. It discloses that the tool 'Returns mortgage application and origination records', which is useful, but does not mention potential behaviors like pagination, rate limits, or error handling when no filter is supplied. The recommendation to limit results hints at data volume but doesn't go further.

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 front-loaded with the core purpose, followed by a concise second sentence on what it returns and a recommendation. The Args list is structured and each parameter explanation earns its place. No fluff or redundancy.

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?

For a data retrieval tool with six parameters and no annotations, the description covers parameter semantics, defaults, and a usage recommendation. However, it lacks details about pagination, max limit enforcement, or behavior when no geographic filter is given. Since an output schema exists, return values are not needed here, but more operational context would make it fully complete.

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 Args section thoroughly explains every parameter: state and county_fips formats, year default, action_taken codes with meanings, loan_type codes, and limit default/max. This adds substantial meaning beyond the bare schema, compensating fully 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 clearly identifies the tool as retrieving 'Home Mortgage Disclosure Act (HMDA) loan-level data', distinguishing it from sibling tools like get_aggregate_stats by explicitly stating 'loan-level data'. The verb 'Get' and resource specificity make the purpose unambiguous.

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?

Provides explicit guidance that 'At least one geographic filter (state or county_fips) is recommended to limit results', which is a clear usage instruction. However, it does not explicitly mention alternatives or when to prefer this over sibling tools, though the loan-level vs aggregate distinction implies it.

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

All HMDA Mortgage Data tools Inspect

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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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