Quickstart

1. Create an API key

Sign in to the Mouse API Console and create a secret key. Verified accounts start with $5.00 in free credits (~50,000 typical decisions, no credit card required).

Export your key in your shell environment:

export MOUSE_API_KEY="sk_mouse_..."

2. Evaluate a state with multiple questions

Send a state (a string, JSON object, or array) and a map of questions (choice, noul, or score) to POST https://api.mouse.dev/v1/decisions (POST /v1/systemone is also supported as an identical alias).

In the example below, we pass a support ticket and evaluate three questions in a single forward pass:

department (choice) — which team should handle the ticket
frustration (score) — where the customer falls on a 3-level rubric (0 to 2)
is_urgent (noul) — the probability (0.0 to 1.0) that the issue is time-sensitive

cURL

curl https://api.mouse.dev/v1/decisions \
  -H "Authorization: Bearer $MOUSE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mouse-1",
    "state": "Our webhook endpoint started returning 500 errors 20 minutes ago and customer orders are stuck.",
    "questions": {
      "department": {
        "type": "choice",
        "instructions": "Which team should handle this ticket?",
        "criteria": {
          "billing": "Invoices, duplicate charges, or refund requests",
          "technical": "Bugs, outages, or API integration failures",
          "sales": "Pricing, enterprise contracts, or account upgrades"
        }
      },
      "frustration": {
        "type": "score",
        "instructions": "How frustrated does the customer appear?",
        "criteria": [
          "Calm, stating facts",
          "Concerned or frustrated but civil",
          "Very angry or threatening to churn"
        ]
      },
      "is_urgent": {
        "type": "noul",
        "instructions": "Does this message describe a production outage or time-sensitive blocker?"
      }
    }
  }'

TypeScript / Node.js

const response = await fetch("https://api.mouse.dev/v1/decisions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.MOUSE_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "mouse-1",
    state:
      "Our webhook endpoint started returning 500 errors 20 minutes ago and customer orders are stuck.",
    questions: {
      department: {
        type: "choice",
        instructions: "Which team should handle this ticket?",
        criteria: {
          billing: "Invoices, duplicate charges, or refund requests",
          technical: "Bugs, outages, or API integration failures",
          sales: "Pricing, enterprise contracts, or account upgrades",
        },
      },
      frustration: {
        type: "score",
        instructions: "How frustrated does the customer appear?",
        criteria: [
          "Calm, stating facts",
          "Concerned or frustrated but civil",
          "Very angry or threatening to churn",
        ],
      },
      is_urgent: {
        type: "noul",
        instructions:
          "Does this message describe a production outage or time-sensitive blocker?",
      },
    },
  }),
});

if (!response.ok) {
  throw new Error(`Mouse API error (${response.status}): ${await response.text()}`);
}

const data = await response.json();
console.log("Department:", data.answers.department.choice, data.answers.department.confidence);
console.log("Frustration (0..2):", data.answers.frustration.score);
console.log("Urgent probability:", data.answers.is_urgent.noul);

Python

import os
import requests

response = requests.post(
    "https://api.mouse.dev/v1/decisions",
    headers={"Authorization": f"Bearer {os.environ['MOUSE_API_KEY']}"},
    json={
        "model": "mouse-1",
        "state": "Our webhook endpoint started returning 500 errors 20 minutes ago and customer orders are stuck.",
        "questions": {
            "department": {
                "type": "choice",
                "instructions": "Which team should handle this ticket?",
                "criteria": {
                    "billing": "Invoices, duplicate charges, or refund requests",
                    "technical": "Bugs, outages, or API integration failures",
                    "sales": "Pricing, enterprise contracts, or account upgrades",
                },
            },
            "frustration": {
                "type": "score",
                "instructions": "How frustrated does the customer appear?",
                "criteria": [
                    "Calm, stating facts",
                    "Concerned or frustrated but civil",
                    "Very angry or threatening to churn",
                ],
            },
            "is_urgent": {
                "type": "noul",
                "instructions": "Does this message describe a production outage or time-sensitive blocker?",
            },
        },
    },
    timeout=5.0,
)
response.raise_for_status()
data = response.json()

print("Department:", data["answers"]["department"]["choice"])
print("Frustration (0..2):", data["answers"]["frustration"]["score"])
print("Urgent probability:", data["answers"]["is_urgent"]["noul"])

3. Inspect the response

mouse-1 returns one typed answer per question key, itemized token usage (state_tokens, question_tokens, framing_tokens, cached_tokens, cost_usd), and the exact immutable snapshot ID (mouse-preview-2026-10-13):

{
  "id": "dec_4c91e02ab718d30f",
  "object": "systemone.response",
  "model": "mouse-preview-2026-10-13",
  "answers": {
    "department": {
      "type": "choice",
      "choice": "technical",
      "confidence": 0.97,
      "probabilities": {
        "technical": 0.98,
        "billing": 0.01,
        "sales": 0.01
      },
      "mouse": {
        "calibrated": false,
        "p_top": 0.98,
        "abstain": null
      }
    },
    "frustration": {
      "type": "score",
      "score": 0.72,
      "confidence": 0.58,
      "legend": {
        "0": "Calm, stating facts",
        "1": "Concerned or frustrated but civil",
        "2": "Very angry or threatening to churn"
      },
      "probabilities": {
        "0": 0.28,
        "1": 0.72,
        "2": 0.0
      },
      "mouse": {
        "calibrated": false,
        "p_top": 0.72,
        "abstain": null
      }
    },
    "is_urgent": {
      "type": "noul",
      "noul": 0.99,
      "mouse": {
        "calibrated": false,
        "p_top": 0.99,
        "abstain": null
      }
    }
  },
  "usage": {
    "input_tokens": 114,
    "state_tokens": 23,
    "question_tokens": 64,
    "framing_tokens": 27,
    "image_tokens": 0,
    "cached_tokens": 0,
    "output_tokens": 0,
    "cost_usd": 0.0000114
  },
  "latency_ms": 41.6
}

4. Branch on answers and confidence in code

Use the returned probabilities and confidence to decide when to act automatically and when to escalate to a human or a reasoning model:

dept = data["answers"]["department"]
urgent = data["answers"]["is_urgent"]["noul"]
frustration = data["answers"]["frustration"]["score"]

if dept["confidence"] < 0.60:
    # Distribution is spread across teams; route to manual triage
    route_to_human_triage(ticket)
elif dept["choice"] == "technical" and urgent >= 0.85:
    page_oncall_engineer(ticket, high_frustration=(frustration >= 1.5))
else:
    assign_to_queue(dept["choice"], ticket)

Key guarantees

Deploy with coding agents: Using Cursor, Claude Code, or Codex? Copy the one-shot setup prompt in Build with coding agents or point your agent at https://www.mouse.dev/llms-full.txt.
Immutable version echo: The model field in the JSON body and the Mouse-Model-Version response header always report the exact snapshot ID (mouse-preview-2026-10-13) even when you send model: "mouse-1".
Loud validation, no silent truncation: States over 65,536 tokens return 422 state_too_long. Validation errors (4xx) are never billed and never echo your request content.
Zero data retention: Every response carries Mouse-Data-Retention: none and Cache-Control: no-store.