HUD Housing Data

get_housing_vouchers

Get Housing Choice Voucher (Section 8) program data by Public Housing Agency.

Returns information about voucher programs administered by PHAs in
the specified state, including total vouchers, utilization rates,
and spending. Queries HUD ArcGIS open data (no auth required).

Args:
    state: Two-letter US state abbreviation (e.g. 'CA', 'NY').
    limit: Maximum number of records to return (default 50, max 500).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`state`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

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 discloses the data source (HUD ArcGIS open data), the auth requirement (none), and the specific return content (total vouchers, utilization rates, spending), conveying a safe read-only retrieval. It doesn't mention rate limits or error behavior, but for a GET-style tool with an output schema, this is a minor 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 compact and front-loaded with a clear one-liner, followed by a brief parameter list. Every sentence earns its place with no filler or repetition of schema details beyond what is helpful.

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

Completeness5/5

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

An output schema exists, so return-value details are covered elsewhere. The description adds the essential data source, auth status, filtering dimension (state), and limit behavior, making it complete for a simple two-parameter 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?

Schema description coverage is 0%, and the description fully compensates. It explains 'state' as a two-letter US abbreviation with examples, and 'limit' as the maximum number of records with default and max values, adding a max constraint (500) not present in the schema.

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 a specific verb+resource: 'Get Housing Choice Voucher (Section 8) program data by Public Housing Agency.' It clearly differentiates from sibling tools by naming the exact HUD program and the type of data returned (vouchers, utilization, spending).

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 makes the tool's scope obvious ('by Public Housing Agency', 'in the specified state') and notes 'no auth required,' which is useful for when it can be invoked. It doesn't explicitly state 'use this vs. sibling X,' but sibling names are distinct enough that the intended use is clear.

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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