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lzinga

US Government Open Data MCP

by lzinga

hud_fair_market_rents

Read-onlyIdempotent

Look up HUD Fair Market Rents by county, metro, or state to see monthly rent limits by bedroom count and determine Section 8 voucher amounts.

Instructions

Get HUD Fair Market Rents (FMR) for a county, metro area, or entire state. Shows monthly rent by bedroom count (efficiency through 4-bedroom). FMR determines Section 8 voucher amounts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoFiscal year (e.g. 2024). Defaults to current year.
stateNoTwo-letter state code for state-wide FMR data (e.g. CA, TX)
entity_idNoCounty FIPS or CBSA code for specific area FMR (get from hud_list_counties)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2026.4.11
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv2026.3.9

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds context about the output content and purpose, but does not disclose additional behavioral traits such as pagination, response format, or any prerequisites. It adds some value but not substantial behavioral transparency beyond annotations.

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 concise, three sentences, front-loaded with the primary action and scope. Every sentence adds useful information: what it gets, what it shows, and why it matters. No wasted words.

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?

For a simple read-only tool with optional parameters, the description covers the key output (rent by bedroom count), geographic scopes, and purpose. It does not explicitly state that state or entity_id must be provided, but that is reasonably inferred from the schema and description. No output schema exists, so the description adequately conveys the return content.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description clarifies that entity_id covers county or metro area and state covers state-wide, which is already implied by the schema descriptions. It does not add significant new parameter semantics beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves HUD Fair Market Rents, specifies geographic scopes (county, metro, state), and explains the output (monthly rent by bedroom count). It distinguishes itself from related tools by focusing on FMR, though it does not explicitly name alternatives like hud_income_limits.

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 when FMR data is needed and even states its purpose (Section 8 voucher amounts), but it does not provide explicit guidance on when to choose this over alternatives, nor does it mention when not to use it. The usage context is clear but not elaborated.

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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