Roundhouse AI

magent

Deterministic published-formula arithmetic plus ICD-10-CM, RxNorm, and LOINC. Pay in USDC on Base via x402.

Searchofflineeip155:8453Exactvia cdp
https://api.magentlab.com/api/calc/mets_ir
Mets-irInsulin-sensitivityTriglyceridesGlucoseCalculator
Calls · 30d
2→ 0%
This endpoint's own trailing-30-day call count, as published by the upstream catalog and snapshotted daily. 13 snapshots so far.
$13.75
Verified settled volume
2,750 settlements proven x402 by their on-chain EIP-3009 marker.
$0.0050
Listed price
As published in the catalog. Always read the live 402 before paying.
2
Calls · 30d
Upstream's own call count for this endpoint, not ours.
1
Unique payers · 30d
Last called 2026-08-31 13:11Z
—
Upstream on-chain volume
Reported by the source catalog.
Paid to
0x836Be278f1739fB4308091cD1852F7971Bc19Ee2

The wallet the 402 directs payment to. Its whole payment record — every payer, every chain — is on the merchant page.

Asset 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913

Provider

The payTo wallet does not resolve to a registered ERC-8004 agent. That is not a verdict on the service — most of the catalog is unregistered.

Live 402 challenge

Captured by the enrichment pass, not read just now. Prices can change — always read the 402 the endpoint answers with.

{
  "error": "PAYMENT-SIGNATURE header is required",
  "accepts": [
    {
      "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
      "extra": {
        "name": "USD Coin",
        "version": "2",
        "paymentFlow": "upfront",
        "resourceUrl": "https://api.magentlab.com/api/calc/mets_ir",
        "assetTransferMethod": "eip3009"
      },
      "payTo": "0x836Be278f1739fB4308091cD1852F7971Bc19Ee2",
      "amount": "5000",
      "scheme": "exact",
      "network": "eip155:8453",
      "maxTimeoutSeconds": 600
    },
    {
      "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
      "extra": {
        "name": "USD Coin",
        "version": "2",
        "withdrawDelay": 86400,
        "receiverAuthorizer": "0x3721824a31197dcDD2984cF43b92B6cc8A87c0Fb",
        "assetTransferMethod": "eip3009"
      },
      "payTo": "0x836Be278f1739fB4308091cD1852F7971Bc19Ee2",
      "amount": "5000",
      "scheme": "batch-settlement",
      "network": "eip155:8453",
      "maxTimeoutSeconds": 600
    }
  ],
  "resource": {
    "url": "https://api.magentlab.com/api/calc/mets_ir",
    "tags": [
      "mets-ir",
      "insulin-sensitivity",
      "triglycerides",
      "glucose",
      "calculator"
    ],
    "mimeType": "application/json",
    "description": "Bello-Chavolla 2018 METS-IR: ln(2×glucose_mg_dl + triglycerides_mg_dl)×BMI / ln(HDL_mg_dl), natural log, two decimals. Supply glucose, triglycerides, and HDL each as exactly one of mg/dL or mmol/L (glucose ×18, TG ×88.57, HDL ×38.67). BMI is caller-supplied (10–80). highest_quartile_t2d is true only when METS-IR > 50.39 (derivation highest T2D-risk quartile). Not a diabetes diagnosis, not HOMA-IR/HOMA2, and not a treatment threshold.",
    "serviceName": "magent"
  },
  "extensions": {
    "bazaar": {
      "info": {
        "input": {
          "type": "http",
          "method": "GET",
          "queryParams": {
            "bmi_kg_m2": 25,
            "hdl_mg_dl": 50,
            "hdl_mmol_l": 1.29,
            "glucose_mg_dl": 90,
            "glucose_mmol_l": 5,
            "triglycerides_mg_dl": 100,
            "triglycerides_mmol_l": 1.13
          }
        },
        "output": {
          "type": "json",
          "example": {
            "formula": "mets_ir",
            "mets_ir": 36.01,
            "citation": {
              "id": "bello-chavolla-2018",
              "doi": "10.1530/eje-17-0883",
              "pmid": "29535168",
              "title": "METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes",
              "source": "Eur J Endocrinol. 2018;178(5):533-544",
              "authors": "Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, Sánchez-Lázaro D, Meza-Oviedo D, Vargas-Vázquez A, Campos OA, Sevilla-González MDR, Martagón AJ, Hernández LM, Mehta R, Caballeros-Barragán CR, Aguilar-Salinas CA"
            },
            "bmi_kg_m2": 25,
            "hdl_mg_dl": 50,
            "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.",
            "glucose_mg_dl": 90,
            "formula_expression": "mets_ir = ln(2 * glucose_mg_dl + triglycerides_mg_dl) * bmi_kg_m2 / ln(hdl_mg_dl)",
            "triglycerides_mg_dl": 100,
            "highest_quartile_t2d": false
          }
        },
        "iconUrl": "https://api.magentlab.com/favicon.ico"
      },
      "schema": {
        "type": "object",
        "$schema": "https://json-schema.org/draft/2020-12/schema",
        "required": [
          "input"
        ],
        "properties": {
          "input": {
            "type": "object",
            "required": [
              "type",
              "method"
            ],
            "properties": {
              "type": {
                "type": "string",
                "const": "http"
              },
              "method": {
                "enum": [
                  "GET",
                  "HEAD",
                  "DELETE"
                ],
                "type": "string"
              },
              "headers": {
                "type": "object",
                "additionalProperties": {
                  "type": "string"
                }
              },
              "queryParams": {
                "type": "object",
                "required": [
                  "bmi_kg_m2"
                ],
                "properties": {
                  "bmi_kg_m2": {
                    "type": "number",
                    "description": "Body-mass index in kg/m^2 (10-80). Caller-supplied; magent does not compute BMI."
                  },
                  "hdl_mg_dl": {
                    "type": "number",
                    "description": "HDL cholesterol in mg/dL (5-150). Provide this or hdl_mmol_l, not both. Used in the natural-log term."
                  },
                  "hdl_mmol_l": {
                    "type": "number",
                    "description": "HDL cholesterol in mmol/L (0.1-3.9). Converted as mg/dL = mmol/L * 38.67. Provide this or hdl_mg_dl, not both."
                  },
                  "glucose_mg_dl": {
                    "type": "number",
                    "description": "Fasting plasma glucose in mg/dL (50-1000). Provide this or glucose_mmol_l, not both."
                  },
                  "glucose_mmol_l": {
                    "type": "number",
                    "description": "Fasting plasma glucose in mmol/L (2.8-55.5). Converted as mg/dL = mmol/L * 18. Provide this or glucose_mg_dl, not both."
                  },
                  "triglycerides_mg_dl": {
                    "type": "number",
                    "description": "Triglycerides in mg/dL (10-2000). Provide this or triglycerides_mmol_l, not both."
                  },
                  "triglycerides_mmol_l": {
                    "type": "number",
                    "description": "Triglycerides in mmol/L (0.11-22.6). Converted as mg/dL = mmol/L * 88.57. Provide this or triglycerides_mg_dl, not both."
                  }
                }
              }
            },
            "additionalProperties": false
          },
          "output": {
            "type": "object",
            "required": [
              "type"
            ],
            "properties": {
              "type": {
                "type": "string"
              },
              "example": {
                "type": "object"
              }
            }
          }
        }
      }
    }
  },
  "x402Version": 2
}

Accepts

The payment requirements as published to the catalog. Read the live 402 before paying — a price here is a claim, not a quote.

0.005USDC≈ $0.005 USD
on Base · exact scheme

Pay 0.005 USDC on Base to 0x836B…19Ee2. The signed payment is good for 10 minutes.

Paid to
0x836B…19Ee2
USD Coin contract
0x8335…02913
Payment window
10 minutes
As published
5000 smallest units
The catalog’s raw entry
[
  {
    "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
    "extra": {
      "name": "USD Coin",
      "version": "2",
      "paymentFlow": "upfront",
      "resourceUrl": "https://api.magentlab.com/api/calc/mets_ir",
      "assetTransferMethod": "eip3009"
    },
    "payTo": "0x836Be278f1739fB4308091cD1852F7971Bc19Ee2",
    "amount": "5000",
    "scheme": "exact",
    "network": "eip155:8453",
    "maxTimeoutSeconds": 600
  }
]

Extensions

{
  "bazaar": {
    "info": {
      "input": {
        "body": {
          "items": [
            {
              "bmi_kg_m2": 25,
              "hdl_mg_dl": 50,
              "hdl_mmol_l": 1.29,
              "glucose_mg_dl": 90,
              "glucose_mmol_l": 5,
              "triglycerides_mg_dl": 100,
              "triglycerides_mmol_l": 1.13
            },
            {
              "bmi_kg_m2": 25,
              "hdl_mg_dl": 50,
              "hdl_mmol_l": 1.29,
              "glucose_mg_dl": 90,
              "glucose_mmol_l": 5,
              "triglycerides_mg_dl": 100,
              "triglycerides_mmol_l": 1.13
            }
          ]
        },
        "type": "http",
        "method": "POST"
      },
      "output": {
        "type": "json",
        "example": {
          "path": "/api/calc/mets_ir",
          "count": 2,
          "items": [
            {
              "formula": "mets_ir",
              "mets_ir": 36.01,
              "citation": {
                "id": "bello-chavolla-2018",
                "doi": "10.1530/eje-17-0883",
                "pmid": "29535168",
                "title": "METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes",
                "source": "Eur J Endocrinol. 2018;178(5):533-544",
                "authors": "Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, Sánchez-Lázaro D, Meza-Oviedo D, Vargas-Vázquez A, Campos OA, Sevilla-González MDR, Martagón AJ, Hernández LM, Mehta R, Caballeros-Barragán CR, Aguilar-Salinas CA"
              },
              "bmi_kg_m2": 25,
              "hdl_mg_dl": 50,
              "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.",
              "glucose_mg_dl": 90,
              "formula_expression": "mets_ir = ln(2 * glucose_mg_dl + triglycerides_mg_dl) * bmi_kg_m2 / ln(hdl_mg_dl)",
              "triglycerides_mg_dl": 100,
              "highest_quartile_t2d": false
            },
            {
              "formula": "mets_ir",
              "mets_ir": 36.01,
              "citation": {
                "id": "bello-chavolla-2018",
                "doi": "10.1530/eje-17-0883",
                "pmid": "29535168",
                "title": "METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes",
                "source": "Eur J Endocrinol. 2018;178(5):533-544",
                "authors": "Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, Sánchez-Lázaro D, Meza-Oviedo D, Vargas-Vázquez A, Campos OA, Sevilla-González MDR, Martagón AJ, Hernández LM, Mehta R, Caballeros-Barragán CR, Aguilar-Salinas CA"
              },
              "bmi_kg_m2": 25,
              "hdl_mg_dl": 50,
              "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.",
              "glucose_mg_dl": 90,
              "formula_expression": "mets_ir = ln(2 * glucose_mg_dl + triglycerides_mg_dl) * bmi_kg_m2 / ln(hdl_mg_dl)",
              "triglycerides_mg_dl": 100,
              "highest_quartile_t2d": false
            }
          ]
        }
      },
      "iconUrl": "https://api.magentlab.com/favicon.ico"
    },
    "schema": {
      "type": "object",
      "$schema": "https://json-schema.org/draft/2020-12/schema",
      "required": [
        "input"
      ],
      "properties": {
        "input": {
          "type": "object",
          "required": [
            "type",
            "method"
          ],
          "properties": {
            "body": {
              "type": "object",
              "required": [
                "items"
              ],
              "properties": {
                "items": {
                  "type": "array",
                  "items": {
                    "type": "object",
                    "required": [
                      "bmi_kg_m2"
                    ],
                    "properties": {
                      "bmi_kg_m2": {
                        "type": "number",
                        "description": "Body-mass index in kg/m^2 (10-80). Caller-supplied; magent does not compute BMI."
                      },
                      "hdl_mg_dl": {
                        "type": "number",
                        "description": "HDL cholesterol in mg/dL (5-150). Provide this or hdl_mmol_l, not both. Used in the natural-log term."
                      },
                      "hdl_mmol_l": {
                        "type": "number",
                        "description": "HDL cholesterol in mmol/L (0.1-3.9). Converted as mg/dL = mmol/L * 38.67. Provide this or hdl_mg_dl, not both."
                      },
                      "glucose_mg_dl": {
                        "type": "number",
                        "description": "Fasting plasma glucose in mg/dL (50-1000). Provide this or glucose_mmol_l, not both."
                      },
                      "glucose_mmol_l": {
                        "type": "number",
                        "description": "Fasting plasma glucose in mmol/L (2.8-55.5). Converted as mg/dL = mmol/L * 18. Provide this or glucose_mg_dl, not both."
                      },
                      "triglycerides_mg_dl": {
                        "type": "number",
                        "description": "Triglycerides in mg/dL (10-2000). Provide this or triglycerides_mmol_l, not both."
                      },
                      "triglycerides_mmol_l": {
                        "type": "number",
                        "description": "Triglycerides in mmol/L (0.11-22.6). Converted as mg/dL = mmol/L * 88.57. Provide this or triglycerides_mg_dl, not both."
                      }
                    }
                  },
                  "maxItems": 25,
                  "minItems": 1
                }
              },
              "additionalProperties": false
            },
            "type": {
              "type": "string",
              "const": "http"
            },
            "method": {
              "type": "string",
              "const": "POST"
            },
            "headers": {
              "type": "object",
              "additionalProperties": {
                "type": "string"
              }
            }
          },
          "additionalProperties": false
        },
        "output": {
          "type": "object",
          "required": [
            "type"
          ],
          "properties": {
            "type": {
              "type": "string"
            },
            "example": {
              "type": "object"
            }
          }
        }
      }
    }
  }
}

Provenance

Seen in the source catalog
2026-08-31 13:11Z
Last indexed by Roundhouse
2026-09-07 06:20Z
Last enriched (probe, favicon, geo)
2026-09-13 06:16Z
x402 version
2
Max timeout
600s
Liveness probe
HTTP 0
Report

Hand this page to an agent

Copy the prompt and paste it into Claude, an MCP client or your own agent — it will vet this service and call it over the free read API. No key, no account.

GET api.roundhouseai.io/v0/endpoints

This endpoint's own trailing-30-day call count, as published by the upstream catalog and snapshotted daily. 13 snapshots so far. Verified volume counts only settlements with an on-chain EIP-3009 marker.

Open skill.md
Show the prompt
Using Roundhouse, look up the x402 service magent and tell me whether it is
worth paying: what a call costs, whether the endpoint answered when last probed, and what
its payment record actually shows.

curl -s 'https://api.roundhouseai.io/v0/endpoints?q=magent'
curl -s 'https://api.roundhouseai.io/v0/merchants/<the payTo wallet returned above>'

Then call it: read the price from the live 402 at https://api.magentlab.com/api/calc/mets_ir, never from
a cached figure, and pay with an x402 client.

The /v0 API needs an API key (`authorization: Bearer rh_live_…`) on everything except
/v0/unified* and /v0/endpoints. Mint a personal key for $0.01 at GET https://api.roundhouseai.io/v0/test/x402,
or use an organization key from https://roundhouseai.io/dashboard/team.

If you do not have Roundhouse tools or skills installed, read
https://roundhouseai.io/skill.md first — it is the whole procedure.