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haksanlulz

mcp-fairrent

by haksanlulz

affordability_check

Check whether a proposed rent is above or below HUD Fair Market Rent for a bedroom size, and determine which income bands a household qualifies under based on annual income and household size.

Instructions

Affordability verdicts computed server-side from the same HUD tables as fmr_lookup and income_limits: whether a proposed rent is above or below the Fair Market Rent for a bedroom size (dollar and percent gap), and which HUD income bands a household qualifies under (extremely low 30%, very low 50% = the Section 8 voucher line, low 80% of area median). Pass rent + bedrooms, income + household_size, or all four; returns the verdicts with the underlying numbers and table year for citation. entityid is a 10-digit county FIPS or a metro CBSA code (same as fmr_lookup).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rentNoProposed monthly rent in dollars, to compare against the FMR for the given bedrooms
yearNoTable year (e.g. '2026'); default is the latest
incomeNoAnnual gross household income in dollars, to compare against the 30/50/80% area-median lines
bedroomsNoBedroom count 0-4 (0 = efficiency/studio); required when rent is given
entityidYes10-digit county entity id (county FIPS + 99999) or metro CBSA code. Derive from a ZIP via zip_crosswalk then list_counties
household_sizeNoFamily size 1-8; required when income is given
Behavior5/5

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

Despite no annotations, the description fully discloses behavior: it returns verdicts with dollar/gap and income band qualifications, includes underlying numbers and table year for citation, and explains entityid format and derivation.

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?

Three concise, front-loaded sentences each serve a purpose: defining the tool, explaining inputs/outputs, and clarifying entityid. No redundant or extraneous text.

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?

The description covers the main outputs and input rules, but lacks mention of default year behavior or error conditions. For a tool with no output schema, it is still very complete.

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?

The description adds significant context beyond the input schema, explaining relational constraints (rent requires bedrooms, income requires household_size) and clarifying entityid usage, even though schema coverage is 100%.

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 it computes affordability verdicts combining FMR and income limit comparisons, and distinguishes from sibling tools fmr_lookup and income_limits by mentioning it uses the same HUD tables but provides combined results.

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 explains the flexible input combinations (rent+bedrooms, income+household_size, or all four) and references sibling tools for deriving entityid, but does not explicitly state when to prefer this tool over fmr_lookup or income_limits individually.

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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