Spec

Fragmentos de código

curl "https://api.oanor.com/entropy-api/v1/meta" \
  -H "x-oanor-key: oanor_test_..."

await fetch("https://api.oanor.com/entropy-api/v1/meta", {
  headers: { "x-oanor-key": "oanor_test_..." }
});

$ch = curl_init("https://api.oanor.com/entropy-api/v1/meta");
curl_setopt($ch, CURLOPT_HTTPHEADER, ["x-oanor-key: oanor_test_..."]);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$out = curl_exec($ch);

import requests
requests.get(
    "https://api.oanor.com/entropy-api/v1/meta",
    headers={"x-oanor-key": "oanor_test_..."}
)

Respuesta de ejemplo

Una respuesta real de este endpoint, capturada en la última comprobación de estado.

{
    "data": {
        "name": "Entropy API",
        "notes": "Shannon entropy H = −Σ p·log2(p) over symbol probabilities; total bits = H × length. Normalized = H ÷ log2(unique symbols). This measures information content — for password strength use a password API, for number statistics use a statistics API, and for grapheme/character counts use a text-segmentation API. Nothing is stored.",
        "version": "v1",
        "endpoints": [
            {
                "path": "/v1/analyze",
                "params": {
                    "text": "the text (required)",
                    "unit": "char (Unicode code points, default) or byte"
                },
                "returns": "Shannon entropy, total bits/bytes, max entropy and normalized score"
            },
            {
                "path": "/v1/frequency",
                "params": {
                    "top": "keep only the N most frequent symbols (default all)",
                    "text": "the text (required)",
                    "unit": "char (default) or byte"
                },
                "returns": "the symbol-frequency distribution"
            },
            {
                "path": "/v1/meta",
                "params": [],
                "returns": "this document"
            }
        ],
        "description": "Measure the information content of text. The analyze endpoint computes the Shannon entropy in bits per symbol, the total information in bits and bytes, the maximum possible entropy for the alphabet used, and a normalized 0–1 score (how uniform/random the distribution is) — over Unicode code points or raw UTF-8 bytes. The frequency endpoint returns the full character-frequency distribution, most common first, with counts and percentages (control characters are shown escaped, bytes as hex). Useful for randomness and password-quality checks, compression estimates, language and cipher analysis, and detecting low-variety or repetitive data. Pure local, no key."
    },
    "meta": {
        "timestamp": "2026-06-03T17:42:21.645Z",
        "request_id": "0b50f57c-7482-4ec5-ac01-76f088f139cb"
    },
    "status": "ok",
    "message": "Meta",
    "success": true
}