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mcpwright
by mcpwright

Get income by ZIP

get_income
Read-only

Fetches adjusted gross income, average per return, and income components (wages, interest, dividends, business income, capital gains) for any U.S. ZIP code from IRS SOI data.

Instructions

Income measures for a ZIP: AGI, average AGI per return, and components.

`zip_code`: a 5-digit US ZIP. Returns total adjusted gross income (AGI),
average AGI per return, and the main income components — salaries and wages,
taxable interest, ordinary dividends, business net income, and net capital
gain. All dollar amounts in USD.

Note: figures cover filed tax returns only. Average AGI is a *mean per
return*, not a median per household, and AGI omits most nontaxable income —
so it is not directly comparable to Census median household income.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zip_codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNo2-letter USPS state code
returnsNoNumber of returns
zipcodeYes5-digit ZIP code
tax_yearYesSOI tax year of the data
total_incomeNoTotal income amount
net_capital_gainNoNet capital gain (less loss) amount
taxable_interestNoTaxable interest amount
avg_agi_per_returnNoAGI divided by the number of returns
ordinary_dividendsNoOrdinary dividends amount
salaries_and_wagesNoSalaries and wages amount
business_net_incomeNoBusiness or professional net income (less loss) amount
adjusted_gross_incomeNoTotal adjusted gross income (AGI)
Behavior5/5

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

The description adds significant behavioral context beyond the readOnly/openWorld annotations: it explains data covers filed tax returns only, average AGI is a mean not median, and AGI omits nontaxable income. These caveats help the agent avoid misinterpretation.

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 well-structured with a summary line, a detailed list of returned components, and a crucial note. Each sentence adds value, and the most important information is front-loaded.

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?

With annotations declaring read-only/open-world and an output schema available, the description covers all essential behavioral and interpretive context. It provides ample detail for a single-parameter 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?

The schema only provides a title 'Zip Code', but the description details that `zip_code` is a 5-digit US ZIP and explains how it is used. This high-value guidance compensates for the schema's lack of description.

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 returns income measures for a ZIP, listing specific components (AGI, average AGI, and income components). It distinguishes itself from siblings by focusing on ZIP-level income aggregates with a specific set of metrics.

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?

Clear context is provided for when to use the tool: when ZIP-level income figures are needed. The note about not being comparable to Census median household income acts as an implicit exclusion, though no explicit alternatives are named.

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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