# Import external results

Source: https://redcrown.ai/docs/guides/import-external-results/

Turn an eval you already ran in any harness into a ranked, shareable RedCrown run.

## 1. Prepare the results JSON

RedCrown expects an aggregate results object with one entry per candidate. The required top-level fields are `name`, `objective`, `quality_metric`, `quality_bar`, `step`, and `candidates`.

**UI**

Go to **Upload** (`/advanced/upload`). Paste your results JSON directly into the text area or load it from a file using **Load file**.

**CLI**

Prepare a `results.json` file in the upload schema. The required fields are `name`, `objective`, `quality_metric`, `quality_bar`, `step`, and `candidates` (an array of per-candidate aggregate scores). See the [CLI reference](../../reference/cli/) for the full schema.

**MCP**

Pass the same object shape to `import_results`. The required fields are `name`, `objective`, `quality_metric`, `quality_bar`, `step`, and `candidates`:

```
{
  "name": "My external eval",
  "objective": "cheapest",
  "quality_metric": "exact_match",
  "quality_bar": 0.8,
  "step": "generate",
  "candidates": [...]
}
```

## 2. Import

Once your results are imported, RedCrown ranks them, applies the same integrity checks as a native run, and makes the run shareable via a proof link.

**UI**

After loading your results on **Upload** (`/advanced/upload`), click **Import**. The ranked run report appears immediately.

**CLI**

```
redcrown import-results results.json
```

The command uploads the results to your workspace and prints the run ID and a link to the ranked report in the app.

**MCP**

Call `import_results` with the full results object. The response includes the **experiment ID** and **run ID**:

```
{
  "name": "My external eval",
  "objective": "cheapest",
  "quality_metric": "exact_match",
  "quality_bar": 0.8,
  "step": "generate",
  "candidates": [...]
}
```

Last verified 2026-07-02.