Income Tax Grounding Data for AI Agents

Overview

An income tax API for US federal and state tax calculation, built for apps that need to calculate taxes from a salary. Income Tax Calculator works by applying the official federal income tax brackets to your gross income after the standard deduction. It calculates the tax owed at each bracket level and returns a detailed breakdown including effective and marginal tax rates. Add fica=true to get take-home pay after the employee share of Social Security (capped at that year's wage base) and Medicare, including the 0.9% Additional Medicare Tax, with the FICA amounts themselves itemized on paid plans. Add state with a two-letter code (any of the 50 states or DC) to get state income tax alongside: the state's standard deduction and personal exemption are taken off the same income, its brackets applied, and any exemption credit subtracted, giving state tax, effective and marginal state rates, combined federal and state tax, and income after both. States with no wage income tax return zero. State figures come from the Tax Foundation's annual state table; phase-outs, recapture, local income taxes and credits beyond the exemption credit are not modelled, and the state's footnotes say where they apply.

Live Test Income Tax Grounding Data for AI Agents Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as IncomeTaxGroundingDataforAIAgents. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

{
  "name": "IncomeTaxGroundingDataforAIAgents",
  "arguments": {
    "income": "85000",
    "rate": "22"
  }
}

You do not name the tool yourself; the model picks it. Asking about 85000 in the terms this source covers is enough for it to reach for IncomeTaxGroundingDataforAIAgents on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

https://api.vervecontext.com/v1/mcp

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

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{
  "status": "ok",
  "error": null,
  "data": {
    "year": 2025,
    "filing_status": "single",
    "income": 85000,
    "standardDeduction": 15000,
    "taxableIncome": 70000,
    "totalTax": 10313.88,
    "effectiveRate": "12.13%",
    "marginalRate": "22%",
    "incomeAfterTax": 74686.12,
    "monthlyTax": 859.49,
    "monthlyIncome": 6223.84,
    "brackets": [
      {
        "rate": 0.1,
        "ratePercent": "10.0%",
        "rangeMin": 0,
        "rangeMax": 11925,
        "taxableAmount": 11926,
        "taxAmount": 1192.6
      },
      {
        "rate": 0.12,
        "ratePercent": "12.0%",
        "rangeMin": 11926,
        "rangeMax": 48475,
        "taxableAmount": 36550,
        "taxAmount": 4386
      },
      {
        "rate": 0.22,
        "ratePercent": "22.0%",
        "rangeMin": 48476,
        "rangeMax": 103350,
        "taxableAmount": 21524,
        "taxAmount": 4735.28
      }
    ],
    "fica": {
      "total": 6502.5,
      "socialSecurityRate": 0.062,
      "socialSecurityWageBase": 176100,
      "socialSecurityTax": 5270,
      "medicareRate": 0.0145,
      "medicareTax": 1232.5,
      "additionalMedicareThreshold": 200000,
      "additionalMedicareTax": 0
    },
    "takeHomePay": 68183.62,
    "monthlyTakeHomePay": 5681.97,
    "totalEffectiveRate": "19.78%"
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at year: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Job Offer Evaluation

When weighing multiple job offers, candidate compensation portals calculate net take-home pay and federal tax obligations across different filing statuses.

Payroll Withholding Estimation

HR platforms estimate employee annual federal tax liability and monthly deductions directly from gross salary inputs to forecast withholdings.

Retirement Distribution Modeling

To model retirement drawdowns, financial advisors project changes in effective and marginal rates as client taxable distributions shift across tax bands.

Custom Rate Invoicing

Compute estimated tax obligations and remaining net earnings for freelance contracts using custom tax percentages and project deductions.

Other ways to use Income Tax Grounding Data for AI Agents

Set up Income Tax Grounding Data for AI Agents on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.

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