Getting Started

What you’ll do

Set an API key in your environment
Make a single request to generate a small dataset
Learn where to look next (API index + reference)

Get an API key

Use the Get Started flow to obtain an API key. Keep it somewhere safe; treat it like a password.

Set your key

export ALEATORIC_API_KEY="your-api-key"

Prerequisites

API key
curl

Generate a small dataset (first success)

curl -sS -X POST "https://mcp.aleatoric.systems/data/generate" \
  -H "X-API-Key: $ALEATORIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "config": { "symbol": "BTC", "seed": 42 },
    "duration_seconds": 60
  }' \
  -o response.json

python3 -c 'import json; print(json.load(open("response.json"))["download_url"])' > download_url.txt
curl -L "$(cat download_url.txt)" -o aleatoric.parquet

Expected output

A file named aleatoric.parquet.
A response.json file containing metadata (including download_url).

Where to go next

Find exact calls quickly: API and API Index
Full request/response shapes: API Reference
Configuration + reproducibility: Presets & Manifests

Features (at a glance)

These are the core capabilities you’ll typically care about as you evaluate fit and get into production integration.

Capability	What you get	Where to learn more
Deterministic reproducibility	Same manifest + seed → identical event sequence across runs and consumption modes.	Determinism, Configuration
Batch datasets (Parquet)	Generate offline datasets for research/backtests and reproducible comparisons.	API Index, API Reference
Live streaming (SSE)	Consume events in real time for integration testing and UI/dev workflows.	API (Common Tasks), API Reference
Venue-aware funding models	Funding mechanics vary by venue (caps/damping/velocity); simulate those differences.	Funding Models
Canonical normalization	Normalize events to a consistent schema for downstream tooling.	API Index, API Reference
Drivers (batch vs stream)	Delivery mode changes how events are emitted, not what events are generated.	Drivers, Determinism
Architecture & data flow	Understand the system boundary and how artifacts/streams fit together.	Architecture