HUD Housing Data

get_homeless_data

Get Point-in-Time (PIT) homeless count data by Continuum of Care region.

Returns homeless population counts from the annual PIT count, broken
down by sheltered/unsheltered status and sub-populations. Data sourced
from HUD Exchange via ArcGIS open data. No API key required.

Args:
    state: Two-letter US state abbreviation (e.g. 'CA', 'WA').
            At least one of state or coc_code must be provided.
    coc_code: Optional Continuum of Care code (e.g. 'WA-500', 'CA-600').
    year: Optional year to filter results.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`state`	No
`coc_code`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries the transparency burden. It discloses the data source (HUD Exchange via ArcGIS), the fact that no API key is required, and the annual PIT count nature. While it does not mention rate limits or pagination, the read-only nature is clear from the verb 'Get' and the context, making the description genuinely informative.

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 moderately detailed but well-organized, with a clear summary followed by an Args section. Each sentence adds value (source, auth, parameter details), though it could be tightened by moving some examples to the schema if it had better coverage. Still, it is efficient and readable.

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 output schema exists, the description need not explain return values. It covers what data is returned, source, authentication, and parameter constraints. It does not specify default behavior for omitted year or interaction when both state and coc_code are given, but these are minor gaps for a 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?

With 0% schema coverage, the description fully compensates by explaining every parameter: state with example format, coc_code with example, and year as optional. It also adds critical constraint information, stating that at least one of state or coc_code must be provided, which is absent from 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 clearly states the tool gets Point-in-Time homeless count data, with a specific resource (PIT counts by CoC region) and explicit breakdowns (sheltered/unsheltered, sub-populations). It is unambiguous and distinct from sibling tools that deal with rents, vouchers, income limits, and public housing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for obtaining homeless count data but does not explicitly contrast with sibling tools or state when to avoid using it. It provides no direct alternatives or exclusions, relying on the distinct resource name to signal appropriate use.

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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