mouse.dev valid

Mouse Decision API (mouse-1)

mouse-1 is a fast (~100ms) single-pass decision model for agent harnesses. Instead of generating text token by token, it reads a state and evaluates up to 128 typed questions (choice, noul, score) in parallel, returning calibrated probability distributions with Zero Data Retention (Mouse-Data-Retention: none) and $0 output token cost.

For agents: Mouse-1 decision API: fast single-pass model for agent harnesses returning calibrated probabilities over typed questions, with zero data retention.

decision apiagent judgellm decisionszero data retentioncalibrated probabilities

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T06:39:33.398Z. Cached copy · Source on mouse.dev

# Mouse Decision API (mouse-1)

> mouse-1 is a fast (~100ms) single-pass decision model for agent harnesses. Instead of generating text token by token, it reads a state and evaluates up to 128 typed questions (choice, noul, score) in parallel, returning calibrated probability distributions with Zero Data Retention (Mouse-Data-Retention: none) and $0 output token cost.

## Quick Start for Coding Agents (Cursor, Claude Code, Codex, OpenCode)

- **Base URL**: `https://api.mouse.dev`
- **Primary Endpoint**: `POST https://api.mouse.dev/v1/decisions`
- **Authentication**: `Authorization: Bearer $MOUSE_API_KEY` (keys start with `sk_mouse_`; create keys with $5 free starter credits at `https://www.mouse.dev/console/keys`).
- **Primary Model Alias**: `mouse-1` (or `mouse`), currently resolving to immutable snapshot `mouse-preview-2026-10-13` (16,384 max `state` tokens).
- **Zero Data Retention**: Every response sends `Mouse-Data-Retention: none` and `Cache-Control: no-store`. Request `state` and `questions` are never logged or stored.
- **Pricing**: `$0.10` / 1M input tokens, `$0.00` output tokens. `4xx` errors are never billed.
- **Wire Compatibility**: Wire-compatible with TypeSafe System One (`@typesafe-ai/sdk` / `typesafe-sdk`) and accepts OpenAI Decisions (`POST /v1/decisions`) field names directly.

### Why Agent Harnesses Use `mouse-1` (The Cost & Latency Lever)

Every turn in an agent harness requires narrow control-flow judgments before and after the main reasoning model runs:
1. **Pre-tool safety gating & auto-approval**: Score `safe` and `destructive` `noul` predicates in ~100 ms before executing `Bash`/`Write`/`MCP` calls. Safe calls run without interrupting the human; destructive calls return `deny` with a reason so the main model re-plans immediately.
2. **Tool & skill routing**: Score candidate MCP tools or skills with a `choice` question so you only load the relevant tool schemas into your frontier model's context window.
3. **Post-tool verification & stall detection**: Score whether test/build output actually supports the agent's completion claim before finishing a turn or burning another frontier model retry.
4. **Prompt-injection defense**: `mouse-1` treats `state` strictly as data (0% decision flip rate on planted instructions vs 65.4% on Jev).

### Endpoints

| Method & Path | Auth | Description |
| --- | --- | --- |
| `POST /v1/decisions` | `Bearer sk_mouse_...` | Evaluate 1 to 128 typed `choice`, `noul`, or `score` questions in parallel against `state` in one request (~100 ms). |
| `POST /v1/calibrations` | `Bearer sk_mouse_...` | Fit a signed confidence cutoff profile (`calp1.<kid>.<blob>`) on 200 to 400 labeled examples (50+ per question signature) using 5-fold cross-fitting. |
| `GET /v1/models` | `Bearer sk_mouse_...` | List active immutable model snapshots and alias mappings (`mouse-1`, `mouse`). |
| `GET /healthz` | None | Unauthenticated liveness and region check (`{ ok: true, region: "us", engine: "modal" }`). |
| `GET /open
…

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