> ## Documentation Index
> Fetch the complete documentation index at: https://sella.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Evaluate Any Dataset Before Buying with try_dataset

> Use try_dataset to inspect any dataset's schema, sample rows, and quality datacard in a closed trial — no payment required, no full content leaked.

Before you purchase a dataset, run `try_dataset` on every serious candidate. The tool opens a closed trial environment and returns preview content, schema coverage, and a full quality datacard — everything you need to make a confident buy decision without spending a cent or exposing paid content.

## Call try\_dataset

Pass the dataset ID from discovery and, optionally, the exact column names your pipeline requires. Sella will report which of those fields are present in the schema, so a mismatch is obvious before you commit.

```json theme={null}
{
  "name": "try_dataset",
  "arguments": {
    "dataset_id": "<id>",
    "required_fields": ["text", "label", "timestamp"]
  }
}
```

<Tip>
  Always pass `required_fields`. Without it, you'll need to scan the schema manually. With it, a missing column surfaces as a clear flag in the trial response — no guesswork.
</Tip>

## Read the datacard

Every Sella listing carries a **datacard** — a structured quality record generated by automated benchmarks and, where applicable, human review. Work through each field before deciding to buy.

<AccordionGroup>
  <Accordion title="qualityStatus" icon="badge-check">
    The top-level verdict from Sella's automated quality pipeline:

    * `passed` — all automated benchmarks are green. Safe to purchase, subject to task fit.
    * `pending` — benchmarks have not run yet. Evaluate the preview rows and review scores manually before buying.
    * `manual_review` — borderline result; the listing is live but flagged for a human moderator. Proceed with extra caution.
    * `failed` — do not buy. The dataset failed objective quality checks.
  </Accordion>

  <Accordion title="structure.temporalRange" icon="calendar">
    The date span the data covers, for example:

    ```json theme={null}
    { "start": "2015-03-01", "end": "2024-11-30" }
    ```

    Compare against your requirement before purchasing. If the range does not cover your target period, skip this dataset and return to discovery.
  </Accordion>

  <Accordion title="review.taskFitnessVector" icon="target">
    Per-task fitness scores assessed by Sella's review pipeline. Use the score for **your specific task**, not the headline number:

    ```json theme={null}
    [
      { "task": "financial-analysis", "score": 0.89, "confidence": "high" },
      { "task": "text-generation",    "score": 0.43, "confidence": "medium" }
    ]
    ```

    A high headline average can hide a low score for the task you actually care about.
  </Accordion>

  <Accordion title="review.syntheticProbability" icon="bot">
    A value from `0.0` (fully human-generated) to `1.0` (fully synthetic). Training on synthetic data can introduce systematic bias and distribution shift. A cautious agent auto-rejects datasets with a score above `0.5` unless the use case explicitly calls for synthetic data.
  </Accordion>

  <Accordion title="review.chinchilla" icon="ruler">
    Whether the dataset's token count suits a target model size — particularly relevant if you plan to run a [Cradle fine-tuning job](/consumers/compute):

    ```json theme={null}
    {
      "tokensForOptimalTraining": 1400000,
      "datasetTokenCount": 800000,
      "verdict": "insufficient"
    }
    ```

    An `insufficient` verdict means you will likely underfit the model. Consider combining multiple datasets before launching a Cradle job.
  </Accordion>
</AccordionGroup>

<Warning>
  **Trust the preview over the scores.** Automated metrics can be gamed; `trial.previewContent` is the ground truth. If the sample rows look noisy, mislabeled, or structurally inconsistent, trust your eyes over a green `qualityStatus`.
</Warning>

## Decision checklist

Work through these steps in order. A dataset that clears all four is ready to purchase.

<Steps>
  <Step title="Schema fits">
    Every field in your `required_fields` list is present in the trial schema response. A single missing column is grounds to skip.
  </Step>

  <Step title="Quality is green">
    `qualityStatus` is `passed`, or the preview rows are convincingly clean and the scores are strong enough to compensate for a `pending` status.
  </Step>

  <Step title="Task fit is high">
    The `taskFitnessVector` score for your specific task clears your minimum threshold. Ignore scores for tasks you are not running.
  </Step>

  <Step title="Temporal range covers your period">
    `temporalRange` spans the date window your pipeline requires. Gaps or a range that ends too early disqualify the dataset.
  </Step>
</Steps>

A dataset that clears all four steps is ready to buy. Head to [Purchase](/consumers/purchase) to retrieve full content and settle payment.