HUD Housing Data

get_income_limits

Get HUD income limits (very low, low, median) by family size.

Returns income limit data used to determine eligibility for HUD
assisted housing programs. Requires HUD User API token.

Args:
    state: Two-letter US state abbreviation (e.g. 'CA', 'NY').
    county_fips: Optional 5-digit county FIPS code (e.g. '06037').
                 If omitted, returns data for all areas in the state.
    year: Income limit 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

A4.2/5.0

Behavior3/5

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 discloses the API token requirement and the fallback behavior for missing county_fips, but does not mention read-only nature, error handling, rate limits, or response format details. While 'Get' implies a safe read, more behavioral specificity would be ideal.

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 purpose sentence followed by an Args block. The first two sentences have slight redundancy ('Get HUD income limits' and 'Returns income limit data'), but overall it is concise and front-loaded. Every sentence adds useful information.

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?

With an output schema present, return values are covered. The description covers the essential parameters, auth, and fallback behavior. It does not clarify what 'by family size' means in relation to the absence of a family_size parameter, which is a minor gap, but overall it is complete enough for a straightforward data retrieval 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?

Despite 0% schema description coverage, the Args section explains all three parameters: state with examples, county_fips with optionality and behavior, and year with default. This adds meaning beyond the bare schema, making it easy to invoke correctly.

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 'Get HUD income limits (very low, low, median) by family size', which identifies the specific resource and action. This distinguishes it from sibling tools like get_fair_market_rents or get_homeless_data by naming HUD income limits directly.

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 provides context about the data being used for HUD eligibility, requires an API token, and explains the behavior when county_fips is omitted (returns all areas in the state). It does not explicitly mention alternative tools or exclusions, but the usage context is clear enough for a data lookup tool.

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

All HUD Housing Data tools Inspect

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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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