API reference

Evaluate a state against a map of typed questions (choice, noul, score) in a single forward pass (~40–100 ms) and receive structured answers with calibrated probability distributions.

Base URL: https://api.mouse.dev (US-East)
Authentication: Authorization: Bearer sk_mouse_... (create an API key)
Data retention: Zero data retention by default on every request (Mouse-Data-Retention: none, Cache-Control: no-store)
OpenAPI 3.1 spec: /openapi.json · Interactive Playground: /console/playground

Method & endpoint	Auth	Description
`POST /v1/decisions`	`Bearer sk_mouse_...`	Evaluate 1 to 128 typed `choice`, `noul` (yes/no), and `score` questions in parallel against a `state` (`POST /v1/systemone` is served as an identical alias).
`POST /v1/calibrations`	`Bearer sk_mouse_...`	Fit a signed confidence cutoff profile (`calp1.`) on 200–5,000 labeled examples using 5-fold out-of-fold cross-fitting.
`GET /v1/models`	`Bearer sk_mouse_...`	List immutable model versions (`mouse-preview-2026-10-13`) and current aliases (`mouse-1`, `mouse`).
`GET /healthz`	None	Unauthenticated liveness and region probe.

`POST /v1/systemone`

Evaluate up to 128 typed questions in parallel and in isolation against a shared state.

POST https://api.mouse.dev/v1/systemone
Authorization: Bearer <MOUSE_API_KEY>
Content-Type: application/json

Request body

Property	Type	Required	Default	Description
`state`	`string \| object \| array \| number \| boolean`	Required	—	The content to evaluate (up to 65,536 tokens). Pass a string for a single message or passage, or a JSON object/array for structured application state, tool proposals, and records. Never truncated; states over 65,536 tokens return `422 state_too_long`.
`questions`	`Record<string, Question>`	Required	—	Non-empty map of 1 to 128 question IDs you choose to typed `ChoiceQuestion`, `NoulQuestion`, or `ScoreQuestion` objects. Question IDs are returned as keys in `answers` and are not sent to the model during inference.
`model`	`string`	Optional	`"mouse-1"`	Model alias (`"mouse-1"`, `"mouse"`) or immutable snapshot ID (`"mouse-preview-2026-10-13"`).
`calibration`	`string \| null`	Optional	`null`	Optional signed calibration profile token (`"calp1.<kid>.<blob>"`) returned by `POST /v1/calibrations`.
`order_invariant`	`boolean`	Optional	`false`	Score every multi-option `choice` question a second time with its options reversed and average the two distributions, removing option-order sensitivity. The reversed questions are billed again (their `question_tokens` and `framing_tokens` roughly double); the `state` prefix is shared and billed once. `noul` and `score` are never permuted.

Unknown top-level fields (for example "temperature") are ignored and listed in the response's warnings array. Unknown fields inside a question object are refused with 422 invalid_schema.

Accepted aliases

So that a migration is a base-URL change, the request parser also accepts, and normalizes before validation:

You send	Read as
`input`	`state`
`questions` as an array of objects carrying `id` or `name`	`questions` map keyed by that `id`/`name`
`type: "predicate"` or `type: "boolean"`	`type: "noul"`
`text`	`instructions`
`options: ["a", "b"]`	`criteria: { "a": null, "b": null }`
`choices: [{ value, description }]`	`criteria: { [value]: description }`
`levels: [{ label, description }]`	`criteria: ["<label>: <description>", ...]`

Aliases change only how the request is read; the response is always the Mouse shape documented below.

Example request (`cURL`, `TypeScript`, `Python`)

curl https://api.mouse.dev/v1/systemone \
  -H "Authorization: Bearer $MOUSE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mouse-1",
    "state": {
      "task": "Rename exported function `parseToken` to `verifyToken` in src/auth.ts.",
      "proposed_command": "pnpm --filter @mouse/relay test"
    },
    "questions": {
      "next_tool": {
        "type": "choice",
        "instructions": "Which tool should handle `task` next?",
        "criteria": {
          "edit_file": "Modify source code in a file",
          "grep_search": "Search for symbols across the repository",
          "run_tests": "Execute the test suite",
          "ask_user": "Ask the user for clarification"
        }
      },
      "safe_to_run": {
        "type": "noul",
        "instructions": "Is `proposed_command` safe to run without human approval?"
      },
      "blast_radius": {
        "type": "score",
        "instructions": "What is the impact of `proposed_command`?",
        "criteria": [
          "Read-only inspection or local test execution",
          "Reversible local workspace edit",
          "Irreversible deletion, remote git push, or external mutation"
        ]
      }
    }
  }'

const res = await fetch("https://api.mouse.dev/v1/systemone", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.MOUSE_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "mouse-1",
    state: {
      task: "Rename exported function `parseToken` to `verifyToken` in src/auth.ts.",
      proposed_command: "pnpm --filter @mouse/relay test",
    },
    questions: {
      next_tool: {
        type: "choice",
        instructions: "Which tool should handle `task` next?",
        criteria: {
          edit_file: "Modify source code in a file",
          grep_search: "Search for symbols across the repository",
          run_tests: "Execute the test suite",
          ask_user: "Ask the user for clarification",
        },
      },
      safe_to_run: {
        type: "noul",
        instructions: "Is `proposed_command` safe to run without human approval?",
      },
      blast_radius: {
        type: "score",
        instructions: "What is the impact of `proposed_command`?",
        criteria: [
          "Read-only inspection or local test execution",
          "Reversible local workspace edit",
          "Irreversible deletion, remote git push, or external mutation",
        ],
      },
    },
  }),
});

const data = await res.json();
console.log(data.answers.next_tool.choice, data.answers.safe_to_run.noul);

import os
import requests

res = requests.post(
    "https://api.mouse.dev/v1/systemone",
    headers={"Authorization": f"Bearer {os.environ['MOUSE_API_KEY']}"},
    json={
        "model": "mouse-1",
        "state": {
            "task": "Rename exported function `parseToken` to `verifyToken` in src/auth.ts.",
            "proposed_command": "pnpm --filter @mouse/relay test",
        },
        "questions": {
            "next_tool": {
                "type": "choice",
                "instructions": "Which tool should handle `task` next?",
                "criteria": {
                    "edit_file": "Modify source code in a file",
                    "grep_search": "Search for symbols across the repository",
                    "run_tests": "Execute the test suite",
                    "ask_user": "Ask the user for clarification",
                },
            },
            "safe_to_run": {
                "type": "noul",
                "instructions": "Is `proposed_command` safe to run without human approval?",
            },
            "blast_radius": {
                "type": "score",
                "instructions": "What is the impact of `proposed_command`?",
                "criteria": [
                    "Read-only inspection or local test execution",
                    "Reversible local workspace edit",
                    "Irreversible deletion, remote git push, or external mutation",
                ],
            },
        },
    },
    timeout=5.0,
)
print(res.json()["answers"])

Question types

A Question is one of three primitives determined by its type field ("choice", "noul", or "score").

All three primitives accept instructions as a string, a structured object, an array, or null. Point a question at a specific nested field inside state (or inside a structured instructions object) using backticks, such as "Does `ticket.messages[0].text` request a refund?".

`choice` question

Picks one option from a set of 1 to 1,024 named options and returns the full probability distribution.

Property	Type	Required	Description
`type`	`"choice"`	Required	Always `"choice"`.
`criteria`	`Record<string, string \| object \| array \| null>`	Required	Map of option keys to rubric descriptions. Use `null` when an option key needs no extra detail. Passing an array instead of an object returns `422 invalid_schema`.
`instructions`	`string \| object \| array \| null`	Optional	What the model should decide.

{
  "department": {
    "type": "choice",
    "instructions": "Which team should handle this ticket?",
    "criteria": {
      "billing": "Payments, invoices, duplicate charges, or refunds",
      "technical": "Bugs, outages, or API integration problems",
      "sales": "Pricing, upgrades, or new accounts"
    }
  }
}

`noul` question

Evaluates a yes/no predicate and returns the probability (0.0 to 1.0) that the statement is true (yes).

Property	Type	Required	Description
`type`	`"noul"`	Required	Always `"noul"`.
`instructions`	`string \| object \| array \| null`	Optional	The yes/no question or statement to evaluate against `state`. Phrase positively so a value near `1.0` means yes.
`criteria`	`{ "true"?: JSONContent, "false"?: JSONContent } \| null`	Optional	Optional descriptions clarifying what `"true"` (yes, near `1.0`) and `"false"` (no, near `0.0`) mean. Only `"true"` and `"false"` keys are allowed.

{
  "is_urgent": {
    "type": "noul",
    "instructions": "Does this message convey time-sensitive urgency?",
    "criteria": {
      "true": "Production outage, blocked customers, or explicit deadline",
      "false": "Routine question, feature request, or non-blocking issue"
    }
  }
}

`score` question

Rates the state along an ordered rubric of 2 to 10 concrete levels (0 to N - 1).

Property	Type	Required	Description
`type`	`"score"`	Required	Always `"score"`.
`criteria`	`Array<string \| object \| array>`	Required	Ordered array of 2 to 10 level descriptions from lowest (`0`) to highest (`N - 1`). Describe concrete situations rather than bare numbers.
`instructions`	`string \| object \| array \| null`	Optional	What dimension the model should rate.

{
  "frustration": {
    "type": "score",
    "instructions": "How frustrated does the customer appear?",
    "criteria": [
      "Calm, stating facts",
      "Frustrated but civil",
      "Very angry or threatening to leave"
    ]
  }
}

Response body (`POST /v1/systemone`)

Property	Type	Description
`id`	`string`	Unique decision identifier (`"dec_..."`).
`object`	`"systemone.response"`	Fixed object type string.
`model`	`string`	Exact immutable model snapshot ID that evaluated the request (`"mouse-preview-2026-10-13"`). Also echoed in the `Mouse-Model-Version` HTTP header.
`answers`	`Record<string, Answer>`	One typed `ChoiceAnswer`, `NoulAnswer`, or `ScoreAnswer` per question, keyed by the same IDs you passed in `questions`.
`usage`	`Usage`	Itemized token counts and exact USD cost for the request.
`latency_ms`	`number`	End-to-end server evaluation latency in milliseconds.
`warnings`	`string[]` (optional)	Present only when unknown top-level request fields were ignored.

Answer types

Every answer carries a type matching its question, the full probabilities distribution, the raw logits behind it (softmax(logits) == probabilities), and a mouse metadata object ({ calibrated, p_top, abstain, confidence_winning, confidence_normalized }). choice and score answers also carry a top-level confidence (0.0 to 1.0).

Log logits and model together if you fit thresholds or calibrators offline; probabilities alone lose the scale.

`choice` answer

Property	Type	Description
`type`	`"choice"`	Always `"choice"`.
`choice`	`string`	The highest-probability option key from `criteria`.
`probabilities`	`Record<string, number>`	Every option key mapped to its probability (`0.0` to `1.0`, summing to `1.0`).
`logits`	`Record<string, number>`	Every option key mapped to its raw log-score.
`confidence`	`number`	Normalized concentration score in `0.0` to `1.0`: ((p_{\max} - 1/k) / (1 - 1/k)) for (k) options (`0.0` for an even split, `1.0` when all probability is on one option).
`mouse`	`MouseAnswerMeta`	`{ calibrated, p_top, abstain, confidence_winning, confidence_normalized }`.

{
  "type": "choice",
  "choice": "edit_file",
  "confidence": 0.88,
  "probabilities": {
    "edit_file": 0.91,
    "grep_search": 0.06,
    "run_tests": 0.02,
    "ask_user": 0.01
  },
  "logits": {
    "edit_file": 2.41,
    "grep_search": -0.31,
    "run_tests": -1.41,
    "ask_user": -2.1
  },
  "mouse": {
    "calibrated": false,
    "p_top": 0.91,
    "abstain": null,
    "confidence_winning": 0.91,
    "confidence_normalized": 0.88
  }
}

`noul` answer

Property	Type	Description
`type`	`"noul"`	Always `"noul"`.
`noul`	`number`	The yes/no probability on a scale from `0.0` (`false` / no) to `1.0` (`true` / yes). A value near `0.5` indicates equal probability for yes and no (certainty is (\|2 \cdot \text{noul} - 1\|), also returned as `mouse.confidence_normalized`).
`probabilities`	`{ "false": number, "true": number }`	The same distribution in the shape `choice` uses: `{ "false": 1 - noul, "true": noul }`.
`logits`	`{ "false": number, "true": number }`	Raw log-scores for each side.
`mouse`	`MouseAnswerMeta`	`{ calibrated, p_top, abstain, confidence_winning, confidence_normalized }`, where `p_top = max(noul, 1 - noul)`.

{
  "type": "noul",
  "noul": 0.98,
  "probabilities": { "false": 0.02, "true": 0.98 },
  "logits": { "false": -1.95, "true": 1.94 },
  "mouse": {
    "calibrated": false,
    "p_top": 0.98,
    "abstain": null,
    "confidence_winning": 0.98,
    "confidence_normalized": 0.96
  }
}

`score` answer

Property	Type	Description
`type`	`"score"`	Always `"score"`.
`score`	`number`	Probability-weighted mean level index: (\sum_{i=0}^{k-1} i \cdot p_i), landing continuously between `0.0` and `k - 1`.
`legend`	`Record<string, JSONContent>`	Each string level index (`"0"`, `"1"`, …) mapped back to its description from `criteria`.
`probabilities`	`Record<string, number>`	Each string level index (`"0"`, `"1"`, …) mapped to its probability (summing to `1.0`).
`logits`	`Record<string, number>`	Each string level index mapped to its raw log-score.
`confidence`	`number`	Ordinal concentration score in `0.0` to `1.0`: (\max(0, 1 - \sum_i p_i \|i - m\| / \text{MAD}_{\text{unif}})), where (m) is the most likely level.
`mouse`	`MouseAnswerMeta`	`{ calibrated, p_top, abstain, confidence_winning, confidence_normalized }`.

{
  "type": "score",
  "score": 0.04,
  "confidence": 0.94,
  "legend": {
    "0": "Read-only inspection or local test execution",
    "1": "Reversible local workspace edit",
    "2": "Irreversible deletion, remote git push, or external mutation"
  },
  "probabilities": {
    "0": 0.96,
    "1": 0.04,
    "2": 0.0
  },
  "logits": {
    "0": 3.18,
    "1": 0.0,
    "2": -6.9
  },
  "mouse": {
    "calibrated": false,
    "p_top": 0.96,
    "abstain": null,
    "confidence_winning": 0.96,
    "confidence_normalized": 0.94
  }
}

`MouseAnswerMeta` object

Property	Type	Description
`calibrated`	`boolean`	`true` when a `calibration` profile was passed on the request and matched this question's signature. When `true`, `probabilities` (and every field derived from them) are temperature-scaled on your labels.
`p_top`	`number`	Top outcome probability ((\max_i p_i)).
`abstain`	`boolean \| null`	`null` when uncalibrated; `true` when `p_top` is below the profile's fitted cutoff (signaling your code to escalate or fall back); `false` when `p_top >= cutoff`.
`confidence_winning`	`number`	The winning-probability convention ((\max_i p_i)), as Cloudflare Clef reports `confidence`. Equal to `p_top`; named so a Clef threshold ports without arithmetic.
`confidence_normalized`	`number`	The normalized convention ((p_{\max} - 1/k) / (1 - 1/k)), as TypeSafe reports `confidence`, on every answer type including `noul`.

Both conventions are present because the same distribution reads differently under each: (0.80, 0.15, 0.05) is 0.80 winning and 0.70 normalized, so a 0.75 threshold flips depending on which one a vendor calls confidence. Threshold on the convention your labels were fitted against, or on the full probabilities.

`Usage` object

Property	Type	Description
`input_tokens`	`integer`	Total billed input tokens: `state_tokens + question_tokens + framing_tokens + image_tokens`.
`state_tokens`	`integer`	Tokens in `state` (counted once per request regardless of how many questions are evaluated).
`question_tokens`	`integer`	Tokens across all question `instructions` and `criteria`.
`framing_tokens`	`integer`	Structural delimiter tokens: `4` per request + `3` per question + `2` per option (`2` options per `noul`).
`image_tokens`	`integer`	`0` for text/JSON requests.
`cached_tokens`	`integer`	`state_tokens` served from the 5-minute in-memory prefix cache (`$0.02 / 1M` instead of `$0.10 / 1M`).
`output_tokens`	`integer`	Always `0` (output is free).
`cost_usd`	`number`	Exact USD cost for the request (`((input_tokens - cached_tokens) * 0.10 + cached_tokens * 0.02) / 1_000_000`).

`POST /v1/calibrations`

Score 200 to 5,000 labeled examples and fit a signed confidence cutoff profile (calp1.<kid>.<blob>) using 5-fold out-of-fold cross-fitting. Examples are evaluated in memory and discarded immediately (Mouse-Data-Retention: none).

POST https://api.mouse.dev/v1/calibrations
Authorization: Bearer <MOUSE_API_KEY>
Content-Type: application/json

Request body

Property	Type	Required	Default	Description
`examples`	`CalExample[]`	Required	—	Array of 200 to 5,000 labeled examples. Each distinct question signature requires 50+ labeled examples to be fitted.
`model`	`string`	Optional	`"mouse-1"`	Model alias or snapshot ID. The returned `profile` is cryptographically bound to the resolved snapshot ID.
`target_error`	`number`	Optional	`0.05`	Maximum acceptable error rate (`0.0 < target_error < 0.5`) among non-abstained predictions.

Each item in examples contains state and questions, where each question includes its ground-truth label:

Labeled choice (CalChoice): label is a string matching one of the option keys in criteria.
Labeled noul (CalNoul): label is a boolean (true or false).
Labeled score (CalScore): label is an integer level index in 0 .. len(criteria) - 1.

curl https://api.mouse.dev/v1/calibrations \
  -H "Authorization: Bearer $MOUSE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mouse-1",
    "target_error": 0.05,
    "examples": [
      {
        "state": "Customer was charged twice for order #1042.",
        "questions": {
          "intent": {
            "type": "choice",
            "instructions": "What does the customer need?",
            "criteria": {
              "billing": "A charge, invoice, or refund issue",
              "shipping": "Delivery status or tracking",
              "account": "Login or profile settings"
            },
            "label": "billing"
          }
        }
      }
    ]
  }'

Response body (`POST /v1/calibrations`)

Property	Type	Description
`object`	`"calibration"`	Fixed object type string.
`model`	`string`	Immutable snapshot ID this profile was fitted against (`"mouse-preview-2026-10-13"`).
`target_error`	`number`	Target error rate used to select the abstention cutoff.
`profile`	`string`	HMAC-SHA256 signed profile token (`"calp1.<kid>.<blob>"`). Pass as `calibration` on `POST /v1/systemone`.
`n_examples`	`integer`	Total labeled examples processed.
`questions`	`CalibrationQuestionReport[]`	Per-question-signature report (`signature`, `type`, `question_ids`, `status`, `before`, and `after` metrics including the fitted `temperature`, `cutoff`, and 5-fold out-of-fold `coverage_at_target_error` and `error_when_covered`).
`note`	`string`	Out-of-fold methodology and zero-data-retention note.
`usage`	`Usage`	Itemized token counts and USD cost for scoring the calibration examples.

{
  "object": "calibration",
  "model": "mouse-preview-2026-10-13",
  "target_error": 0.05,
  "profile": "calp1.k1.eyJmaXRzIjpb...",
  "n_examples": 250,
  "questions": [
    {
      "signature": "4f8c12a90e3b7104",
      "type": "choice",
      "question_ids": ["intent"],
      "status": "fitted",
      "before": {
        "accuracy": 0.912,
        "ece": 0.031,
        "brier": 0.142,
        "nll": 0.264,
        "mean_confidence": 0.938,
        "coverage": 1.0,
        "cutoff": null,
        "n": 250
      },
      "after": {
        "accuracy": 0.912,
        "ece": 0.031,
        "brier": 0.142,
        "nll": 0.264,
        "mean_confidence": 0.938,
        "coverage": 0.864,
        "cutoff": 0.78,
        "n": 250,
        "coverage_at_target_error": 0.856,
        "error_when_covered": 0.0421
      }
    }
  ],
  "note": "'coverage_at_target_error' and 'error_when_covered' are 5-fold out-of-fold. Examples were not stored.",
  "usage": {
    "input_tokens": 14500,
    "state_tokens": 3500,
    "question_tokens": 7750,
    "framing_tokens": 3250,
    "image_tokens": 0,
    "cached_tokens": 0,
    "output_tokens": 0,
    "cost_usd": 0.00145
  }
}

`GET /v1/models`

List active immutable model versions and alias mappings (mouse-1, mouse).

curl https://api.mouse.dev/v1/models \
  -H "Authorization: Bearer $MOUSE_API_KEY"

{
  "object": "list",
  "alias": {
    "mouse-1": "mouse-preview-2026-10-13",
    "mouse": "mouse-preview-2026-10-13"
  },
  "data": [
    {
      "id": "mouse-preview-2026-10-13",
      "released": "2026-10-13",
      "status": "preview",
      "base": "mouse-1",
      "max_state_tokens": 65536,
      "modalities": ["text"],
      "retire_after": null
    }
  ],
  "today": "2026-10-14"
}

`GET /healthz`

Unauthenticated liveness probe returning serving region and engine status.

curl https://api.mouse.dev/healthz

{
  "ok": true,
  "region": "us",
  "engine": "modal"
}

Response headers

Every response from https://api.mouse.dev includes these HTTP headers:

Header	Example	Description
`Mouse-Request-Id`	`req_8f92a1c04e1b902d73c104ab`	Unique request identifier (`req_` + 24 hex chars).
`Mouse-Model-Version`	`mouse-preview-2026-10-13`	Exact immutable snapshot ID that scored the request (`POST /v1/systemone` and `POST /v1/calibrations`).
`Mouse-Region`	`us`	Serving region (`us`).
`Mouse-Data-Retention`	`none`	Enforced zero data retention on every response.
`Cache-Control`	`no-store`	Prevents intermediary caching.
`x-ratelimit-limit-requests`	`200`	Sustained requests-per-second limit (`200` standard, `5` playground).
`x-ratelimit-remaining-requests`	`399`	Remaining requests in the burst bucket (`400` capacity).
`x-ratelimit-reset-requests`	`1`	Seconds until the request bucket resets (`1`).
`x-ratelimit-limit-tokens`	`20000000`	Input-tokens-per-minute limit (`20,000,000`).
`x-ratelimit-remaining-tokens`	`19999926`	Remaining input tokens in the 60-second bucket.
`x-ratelimit-reset-tokens`	`60`	Seconds until the token bucket resets (`60`).
`Retry-After`	`2`	Present on `429` and `503` responses; integer seconds to wait before retrying.

Errors

All non-2xx responses use a single JSON error envelope. Messages name the limit or offending field and never echo request content:

{
  "error": {
    "type": "invalid_request_error",
    "code": "state_too_long",
    "message": "state is 71,204 tokens; mouse-preview-2026-10-13 reads at most 65,536. Nothing was truncated or billed.",
    "param": "state"
  }
}

Status	`error.type`	`error.code`	Meaning
`400`	`invalid_request_error`	`invalid_calibration_profile`	Malformed, altered, or retired `calibration` profile token.
`401`	`authentication_error`	`missing_api_key`, `invalid_api_key`	Missing `Authorization: Bearer sk_mouse_...` header, bad CRC32 checksum, or revoked/expired key.
`402`	`billing_error`	`insufficient_credits`	Prepaid credit balance is `$0.00`. Add credits at `/console/billing`.
`404`	`invalid_request_error`	`model_not_found`	Unknown `model` name. See `GET /v1/models`.
`409`	`invalid_request_error`	`profile_model_mismatch`	`calibration` profile was fitted on a different model snapshot than the one serving the request.
`413`	`invalid_request_error`	`request_too_large`	Request body exceeds 4,000,000 bytes (4 MB).
`422`	`invalid_request_error`	`invalid_schema`, `state_too_long`, `too_many_questions`, `too_many_options`, `too_few_examples`, `too_many_examples`, `too_few_examples_per_question`	Request failed schema validation or exceeded a published payload limit. Never billed.
`429`	`rate_limit_error`	`rate_limited`, `token_rate_limited`	Exceeded 200 RPS (burst 400) or 20M TPM. Honor the `Retry-After` header.
`503`	`api_error`	`engine_timeout`	Inference worker timed out (`10s`). Honor the `Retry-After` header.

API reference

POST /v1/systemone

Request body

Accepted aliases

Example request (cURL, TypeScript, Python)

Question types

choice question

noul question

score question

Response body (POST /v1/systemone)

Answer types

choice answer

noul answer

score answer

MouseAnswerMeta object

Usage object

POST /v1/calibrations

Request body

Response body (POST /v1/calibrations)

GET /v1/models

GET /healthz

Response headers

Errors

`POST /v1/systemone`

Example request (`cURL`, `TypeScript`, `Python`)

`choice` question

`noul` question

`score` question

Response body (`POST /v1/systemone`)

`choice` answer

`noul` answer

`score` answer

`MouseAnswerMeta` object

`Usage` object

`POST /v1/calibrations`

Response body (`POST /v1/calibrations`)

`GET /v1/models`

`GET /healthz`