HUD Housing Data

get_public_housing

Get public housing buildings and developments in a state.

Queries HUD's open ArcGIS data for public housing inventory.
No API key required. Returns building names, addresses, unit counts,
and managing Public Housing Agency information.

Args:
    state: Two-letter US state abbreviation (e.g. 'WA', 'TX').
    city: Optional city name to filter results.
    limit: Maximum number of records to return (default 50, max 500).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`city`	No
`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, but the description carries the burden well. It discloses that the tool 'Queries HUD's open ArcGIS data' (read-only behavior), 'No API key required' (auth requirements), and lists the return content. This goes beyond the schema and gives the agent confidence that nothing is modified.

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 efficient and front-loaded. The first sentence gives the primary purpose. Each subsequent sentence adds essential detail (data source, auth, return content, parameter explanations) without any redundancy or fluff.

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?

Given the tool's simplicity and the presence of an output schema, the description covers all necessary aspects: data source, auth requirements, parameter semantics, and return content. Nothing critical is missing for an agent to correctly select and invoke the 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%, but the description compensates fully. It explains state as a two-letter abbreviation with examples, city as an optional filter, and limit with default and max values. This adds critical meaning that the bare schema properties lack.

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 explicitly states 'Get public housing buildings and developments in a state,' which is a specific verb+resource with scope. It further elaborates that it queries HUD's open ArcGIS data and returns building names, addresses, unit counts, and PHA info, clearly distinguishing it from siblings like get_fair_market_rents.

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 the tool: when public housing inventory data is needed for a state, with optional city and limit filters. It does not explicitly name alternatives or exclusions, but the focus on public housing buildings and developments makes its use case unambiguous among the housing-related siblings.

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.

Resources

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