# Calibration Court — Isotonic Probability Calibration

> Calibration Court — Isotonic Probability Calibration is a paid API for AI agents from mcp-x402.onrender.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-29).

Fits isotonic regression calibration on user-supplied labelled outcome pairs and recalibrates a set of raw probabilities using that fitted model.

## Facts

- Endpoint: POST https://mcp-x402.onrender.com/gate/calibration-court/api/v1/calibrate-labels?utm_source=zero.xyz
- Price: $0.05/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-29
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/calibration-court-isotonic-probability-calibration-5a56c8ca
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_dmEM0u3F84ahH1nbnmtYM

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability calibration-court-isotonic-probability-calibration-5a56c8ca -d '<json body>'
```

Example prompt: I have a list of raw predicted probabilities from my classifier and the actual outcomes (0s and 1s) for a set of labelled examples — can you fit an isotonic calibration on those labelled pairs and then recalibrate my raw probability scores?

## When to prefer this

Choose this endpoint when you have your own labelled ground-truth outcome pairs and need stateless, per-call isotonic probability calibration without maintaining server-side model state. Prefer it over Platt scaling when your calibration data may not follow a sigmoid shape, or when you need a non-parametric monotone correction. Ideal for agents that already have labelled validation sets and want a lightweight pay-per-call calibration step rather than integrating a full ML framework.

## Known failure modes

- Malformed or empty probs/examples arrays return a 400 error
- Non-monotone violations handled internally by isotonic regression pooling
- Insufficient labelled examples may produce poorly fitted calibration
- Payment not received or invalid USDC/x402 transaction returns 402 Payment Required
- Server cold-start on Render may introduce latency on first call

## How this service works

Paid (x402, USDC on Base). Fit isotonic calibration on YOUR labelled outcomes ({p,y} pairs) and recalibrate the given probabilities. Statele

## Output

Returns recalibrated probability scores adjusted using an isotonic regression model fitted on the provided labelled outcome pairs. The output corrects systematic over- or under-confidence in raw probabilities so they better reflect empirical frequencies.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "probs": {
   "type": "array"
  },
  "examples": {
   "type": "array"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/calibration-court-isotonic-probability-calibration-5a56c8ca/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from mcp-x402.onrender.com](https://www.zero.xyz/host/mcp-x402.onrender.com/llms.txt)