HUD Housing Data

get_fair_market_rents

Get HUD Fair Market Rent (FMR) values for 0-4 bedroom units.

Returns FMR data used to determine Housing Choice Voucher payment
standards and other HUD program parameters. Requires HUD User API
token; returns registration guidance if token is not configured.

Args:
    state: Two-letter US state abbreviation (e.g. 'CA', 'WA').
    county_fips: Optional 5-digit county FIPS code (e.g. '06037' for LA County).
                 If omitted, returns statewide FMR data.
    year: FMR fiscal year (default 2024).

Input Schema

TableJSON Schema

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

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states the tool requires a HUD User API token and 'returns registration guidance if token is not configured', which is a valuable behavioral trait. It also explains the county_fips default behavior (statewide data when omitted). However, it does not cover error handling, rate limits, or response details, warranting a 4 rather than a 5.

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 front-loaded with the core action, then provides a brief usage context, token requirement, and a structured Args section. It is appropriately sized for a three-parameter tool, though the opening two sentences overlap slightly and could be condensed.

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's moderate complexity (3 parameters, output schema exists), the description covers purpose, token auth, parameter semantics, and scoping behavior. The output schema handles return-value documentation, so the description is sufficiently complete for selection and invocation. The only minor gap is explicit comparison to sibling tools, which is covered under usage guidelines.

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 input schema has zero description coverage, so the description must fully compensate. It thoroughly explains each parameter: state as a two-letter abbreviation with examples, county_fips as an optional 5-digit code with LA County example and statewide fallback, and year as fiscal year with default 2024. This adds substantial meaning beyond the raw property names.

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 HUD Fair Market Rent (FMR) values for 0-4 bedroom units', a specific verb and resource that clearly distinguishes it from sibling tools like get_income_limits or get_housing_vouchers. It also explains the programmatic purpose (Housing Choice Voucher payment standards), reinforcing the tool's identity.

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 clear context for when to use this tool via 'used to determine Housing Choice Voucher payment standards and other HUD program parameters'. It also discloses the API token prerequisite and fallback behavior, but it does not explicitly name alternatives or exclusion criteria, so it stops short of a 5.

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

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct HUD dataset: fair market rents, homeless counts, housing vouchers, income limits, and public housing inventory. There is no overlap in purpose or data returned, so an agent can clearly select the right tool.

Naming Consistency5/5

All tool names follow the consistent verb_noun pattern of get_<data_type>. This predictable naming makes it easy to infer functionality and extend to additional datasets.

Tool Count5/5

With 5 tools, the server is well-scoped for a data access API focused on HUD housing datasets. The count is in the ideal range and each tool earns its place by covering a major data category.

Completeness4/5

The server covers five key HUD housing datasets, but omits some common ones like multifamily housing properties or Section 8 contract data. Minor gaps exist but the core housing data lifecycle (rents, limits, vouchers, public housing, homelessness) is well represented.

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