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mcpwright
by mcpwright

Get credits by ZIP

get_credits
Read-only

Retrieve Earned Income Tax Credit and additional child tax credit counts and amounts for a U.S. ZIP code from IRS SOI data. See refundable-credit take-up for any area.

Instructions

Refundable-credit take-up for a ZIP: the EITC and additional child tax credit.

`zip_code`: a 5-digit US ZIP. Returns the number of returns and total amount
for the Earned Income Tax Credit (overall and split by number of qualifying
children: none / one / two / three or more), plus the additional (refundable)
child tax credit. Amounts in USD.

Note: a 0 may be IRS-suppressed (<20 returns in each AGI-bracket cell)
rather than a true zero — it does not prove no one claims the credit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zip_codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNo2-letter USPS state code
zipcodeYes5-digit ZIP code
tax_yearYesSOI tax year of the data
eitc_amountNoTotal EITC amount (USD)
eitc_returnsNoNumber of returns claiming the EITC
eitc_by_childrenYesEITC returns and amount split by number of qualifying children
additional_ctc_amountNoAdditional (refundable) child tax credit amount (USD)
additional_ctc_returnsNoNumber of returns with the additional (refundable) child tax credit
Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds valuable behavioral context beyond annotations, notably the IRS-suppression caveat where a zero may not be a true zero, plus the output components and USD units. This significantly helps interpretation.

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 tightly structured: purpose sentence, parameter and return detail, then an essential caveat. Every sentence earns its place, and the most critical information is front-loaded. No redundant or filler content.

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 an output schema present, detailed return structure is covered externally. The description provides the purpose, the one parameter's format, the breakdown logic, and a crucial data-quality caveat, making it complete for an agent to select and invoke the tool correctly.

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 coverage is 0%, but the description compensates by explicitly defining `zip_code` as a '5-digit US ZIP', adding format and meaning beyond the schema's bare string type. With only one required parameter, this fully covers the parameter semantics.

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 opens with 'Refundable-credit take-up for a ZIP' and explicitly names the EITC and additional child tax credit, clearly distinguishing this from sibling tools focused on income, AGI, deductions, or filing status. It uses a specific verb and resource, making the tool's purpose immediately identifiable.

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 clearly implies use when ZIP-level refundable credit data is needed, and enumerates the exact breakdowns returned (e.g., by number of qualifying children). It does not explicitly name alternative tools or state when not to use it, so it stops short of full exclusion guidance, but the context is clear.

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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