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Agent Service · x402 · early usage

Khan 2019 PCP-HF 10-year incident heart-failure percent for age 30-79 from sex, caller-…

Khan 2019 PCP-HF 10-year incident heart-failure percent for age 30-79 from sex, caller-assigned white or black cohort, treated or untreated systolic BP and fasting glucose, total and HDL cholesterol, BMI, smoking, and QRS duration. Table 2 coefficients. Calle…

by api.magentlab.com · discovered, not yet claimed by its owner · updated 2026-10-13

30d calls5
30d payers2
MethodPOST
Outputjson

Mirrored from Coinbase's public x402 Bazaar. The description is the provider's own text and has not been reviewed here; the payment requirements are copied as published.

What it does

Khan 2019 PCP-HF 10-year incident heart-failure percent for age 30-79 from sex, caller-assigned white or black cohort, treated or untreated systolic BP and fasting glucose, total and HDL cholesterol, BMI, smoking, and QRS duration. Table 2 coefficients. Caller assigns cohort; magent does not assign race. Not PREVENT 2024, not 2013 PCE, not a diagnosis or treatment order. magent does not assay lipids, measure blood pressure, or interpret an ECG.

Tags: pcp-hf · heart-failure · hemodynamics · cvd-risk · calculator · bazaar

Call contract

Method
POST
Input
HTTP request
Output
json
Last paid call
2026-10-13
Input example from Bazaar metadata
{
  "items": [
    {
      "age": 50,
      "antihypertensive": false,
      "bmi_kg_m2": 22,
      "cohort": "white",
      "diabetes_medication": false,
      "glucose_mg_dl": 90,
      "hdl_mg_dl": 55,
      "qrs_ms": 90,
      "sbp_mm_hg": 120,
      "sex": "female",
      "smoker": false,
      "total_cholesterol_mg_dl": 180
    },
    {
      "age": 50,
      "antihypertensive": false,
      "bmi_kg_m2": 22,
      "cohort": "white",
      "diabetes_medication": false,
      "glucose_mg_dl": 90,
      "hdl_mg_dl": 55,
      "qrs_ms": 90,
      "sbp_mm_hg": 120,
      "sex": "female",
      "smoker": false,
      "total_cholesterol_mg_dl": 180
    }
  ]
}
Output example
{
  "count": 2,
  "items": [
    {
      "age": 50,
      "antihypertensive": false,
      "bmi_kg_m2": 22,
      "citation": {
        "authors": "Khan SS, Ning H, Shah SJ, Yancy CW, Carnethon M, Berry JD, et al.",
        "doi": "10.1016/j.jacc.2019.02.057",
        "id": "khan-2019-pcp-hf",
        "pmid": "31097157",
        "source": "J Am Coll Cardiol. 2019;73(19):2388-2397",
        "title": "10-Year Risk Equations for Incident Heart Failure in the General Population"
      },
      "cohort": "white",
      "diabetes_medication": false,
      "disclaimer": "Not a medical device. Deterministic published-formula or published-code output for autonomous agents. A licensed clinician remains responsible for patient care and billing submissions.",
      "formula": "pcp_hf",
      "formula_expression": "10-year incident HF % = 100*(1-S0**exp(IndX-MeanCV)); Khan 2019 Table 2; Ln=natural log; age 30-79; cohort white|black (caller-assigned paper terms; not param race); antihypertensive true uses treated SBP terms only; diabetes_medication true uses treated glucose terms only (not both); interactions only where Table 2 is not N/A; WHITE MALE: ln_age 41.94, ln_age^2 -0.88, ln_treated_sbp 1.03, ln_untreated_sbp 0.91, smoker 0.74, ln_treated_glucose 0.90, ln_untreated_glucose 0.78, ln_tc 0.49, ln_hdl -0.44, ln_bmi 37.2, ln_age*ln_bmi -8.83, ln_qrs 0.63, MeanCV 171.5, S0 0.98752; WHITE FEMALE: ln_age 20.55, ln_treated_sbp 12.95, ln_age*ln_treated_sbp -2.96, ln_untreated_sbp 11.86, ln_age*ln_untreated_sbp -2.73, smoker 11.02, ln_age*smoker -2.50, ln_treated_glucose 1.04, ln_untreated_glucose 0.91, ln_hdl -0.07, ln_bmi 1.33, ln_qrs 1.06, MeanCV 99.73, S0 0.99348; BLACK MALE: ln_age 2.88, ln_treated_sbp 2.31, ln_untreated_sbp 2.17, smoker 1.66, ln_age*smoker -0.25, ln_treated_glucose 0.64, ln_untreated_glucose 0.58, ln_hdl -0.81, ln_bmi 1.16, ln_qrs 0.73, MeanCV 28.73, S0 0.98295; BLACK FEMALE: ln_age 51.75, ln_treated_sbp 29.0, ln_age*ln_treated_sbp -6.59, ln_untreated_sbp 28.18, ln_age*ln_untreated_sbp -6.42, smoker 0.76, ln_treated_glucose 0.97, ln_untreated_glucose 0.80, ln_tc 0.32, ln_bmi 21.24, ln_age*ln_bmi -5.0, ln_qrs 1.27, MeanCV 233.9, S0 0.99260; round 1 decimal; not PREVENT 2024; not 2013 PCE; not a diagnosis",
      "glucose_mg_dl": 90,
      "hdl_mg_dl": 55,
      "individual_sum": 98.7374,
      "qrs_ms": 90,
      "sbp_mm_hg": 120,
      "sex": "female",
      "smoker": false,
      "ten_year_hf_percent": 0.2,
      "total_cholesterol_mg_dl": 180
    },
    {
      "age": 50,
      "antihypertensive": false,
      "bmi_kg_m2": 22,
      "citation": {
        "authors": "Khan SS, Ning H, Shah SJ, Yancy CW, Carnethon M, Berry JD, et al.",
        "doi": "10.1016/j.jacc.2019.02.057",
        "id": "khan-2019-pcp-hf",
        "pmid": "31097157",
        "source": "J Am Coll Cardiol. 2019;73(19):2388-2397",
        "title": "10-Year Risk Equations for Incident Heart Failure in the General Population"
      },
      "cohort": "white",
      "diabetes_medication": false,
      "disclaimer": "Not a medical device. Deterministic published-formula or published-code output for autonomous agents. A licensed clinician remains responsible for patient care and billing submissions.",
      "formula": "pcp_hf",
      "formula_expression": "10-year incident HF % = 100*(1-S0**exp(IndX-MeanCV)); Khan 2019 Table 2; Ln=natural log; age 30-79; cohort white|black (caller-assigned paper terms; not param race); antihypertensive true uses treated SBP terms only; diabetes_medication true uses treated glucose terms only (not both); interactions only where Table 2 is not N/A; WHITE MALE: ln_age 41.94, ln_age^2 -0.88, ln_treated_sbp 1.03, ln_untreated_sbp 0.91, smoker 0.74, ln_treated_glucose 0.90, ln_untreated_glucose 0.78, ln_tc 0.49, ln_hdl -0.44, ln_bmi 37.2, ln_age*ln_bmi -8.83, ln_qrs 0.63, MeanCV 171.5, S0 0.98752; WHITE FEMALE: ln_age 20.55, ln_treated_sbp 12.95, ln_age*ln_treated_sbp -2.96, ln_untreated_sbp 11.86, ln_age*ln_untreated_sbp -2.73, smoker 11.02, ln_age*smoker -2.50, ln_treated_glucose 1.04, ln_untreated_glucose 0.91, ln_hdl -0.07, ln_bmi 1.33, ln_qrs 1.06, MeanCV 99.73, S0 0.99348; BLACK MALE: ln_age 2.88, ln_treated_sbp 2.31, ln_untreated_sbp 2.17, smoker 1.66, ln_age*smoker -0.25, ln_treated_glucose 0.64, ln_untreated_glucose 0.58, ln_hdl -0.81, ln_bmi 1.16, ln_qrs 0.73, MeanCV 28.73, S0 0.98295; BLACK FEMALE: ln_age 51.75, ln_treated_sbp 29.0, ln_age*ln_treated_sbp -6.59, ln_untreated_sbp 28.18, ln_age*ln_untreated_sbp -6.42, smoker 0.76, ln_treated_glucose 0.97, ln_untreated_glucose 0.80, ln_tc 0.32, ln_bmi 21.24, ln_age*ln_bmi -5.0, ln_qrs 1.27, MeanCV 233.9, S0 0.99260; round 1 decimal; not PREVENT 2024; not 2013 PCE; not a diagnosis",
      "glucose_mg_dl": 90,
      "hdl_mg_dl": 55,
      "individual_sum": 98.7374,
      "qrs_ms": 90,
      "sbp_mm_hg": 120,
      "sex": "female",
      "smoker": false,
      "ten_year_hf_percent": 0.2,
      "total_cholesterol_mg_dl": 180
    }
  ],
  "path": "/api/calc/pcp_hf"
}

Use with an agent

  1. Discover. Agents find this listing with GET https://bazaar.saylorinnovations.com/discovery/search?query=…, the MCP tool search_resources, or /discovery/resources.
  2. Inspect. Fetch /resources/38975.json for the price and payment options.
  3. Pay. Call GET https://api.magentlab.com/api/calc/pcp_hf. The provider answers 402 with its payment requirements. An x402 client signs one of the options below and retries with PAYMENT-SIGNATURE.
  4. Execute. The provider returns 200 with the data. Agent Bazaar is not in the request path and never sees your payment.

For humans

You don't need an account or an API key; you need a wallet holding a small amount of USDC on Base, and an x402-capable client (see the examples below). Call the endpoint unpaid first to see exactly what it asks for.

Pricing & payment

Price
$0.005 / request
Protocol
x402 v2
Auth
No account or API key. Pay per request.
Networks
Base
Demand
5 calls · 2 unique payers in 30d (public x402 Bazaar data)

x402 payment requirements

SchemeNetworkAssetAmountPay toTimeout
exactBase
eip155:8453		USDC$0.0050x836Be278f1739fB4308091cD1852F7971Bc19Ee2600s
accepts[] (raw JSON)
[
  {
    "scheme": "exact",
    "network": "eip155:8453",
    "payTo": "0x836Be278f1739fB4308091cD1852F7971Bc19Ee2",
    "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
    "amount": "5000",
    "amountUsd": 0.005,
    "maxTimeoutSeconds": 600
  }
]

Schemas

Output schema (JSON Schema)
{
  "properties": {
    "example": {
      "type": "object"
    },
    "type": {
      "type": "string"
    }
  },
  "required": [
    "type"
  ],
  "type": "object"
}
Example shape — generated from the schema, not a captured live response
{
  "example": {},
  "type": "string"
}

Examples

curl
curl "https://api.magentlab.com/api/calc/pcp_hf"
# -> 402 Payment Required, accepts[] lists how to pay
# retry with a PAYMENT-SIGNATURE (or PAYMENT header) once paid
JavaScript
const res = await fetch("https://api.magentlab.com/api/calc/pcp_hf");
if (res.status === 402) {
  const { accepts } = await res.json();
  // pay one of accepts[] via an x402 client, then retry with the payment header
}
Python
import httpx
res = httpx.get("https://api.magentlab.com/api/calc/pcp_hf")
if res.status_code == 402:
    accepts = res.json()["accepts"]
    # pay one of accepts[] via an x402 client, then retry with the payment header

Details

Provider
api.magentlab.com
Endpoint
https://api.magentlab.com/api/calc/pcp_hf
Machine-readable
/resources/38975.json