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# Tone consistency scorer

The `createToneScorer()` function evaluates the text's emotional tone and sentiment consistency. It can operate in two modes: comparing the agent output's sentiment against a configured `referenceTone`, or analyzing tone stability across the sentences of the agent output.

## Parameters

The `createToneScorer()` function accepts an optional config object with the following properties:

**referenceTone** (`string`): Reference text whose sentiment the agent output is compared against. When omitted, the scorer measures sentiment stability across the output's sentences instead.

This function returns an instance of the MastraScorer class. See the [MastraScorer reference](https://mastra.ai/reference/evals/mastra-scorer) for details on the `.run()` method and its input/output.

## `.run()` returns

**runId** (`string`): The id of the run (optional).

**preprocessStepResult** (`object`): Object with tone metrics: { score: number, responseSentiment: number, referenceSentiment: number, difference: number } (for comparison mode) OR { score: number, avgSentiment: number, sentimentVariance: number } (for stability mode)

**score** (`number`): Tone consistency/stability score (0-1).

`.run()` returns a result in the following shape:

```typescript
{
  runId: string,
  preprocessStepResult: {
    score: number,
    responseSentiment?: number,
    referenceSentiment?: number,
    difference?: number,
    avgSentiment?: number,
    sentimentVariance?: number,
  },
  score: number
}
```

## Scoring details

The scorer evaluates sentiment consistency through tone pattern analysis and mode-specific scoring.

### Scoring Process

1. Analyzes tone patterns:

   - Extracts sentiment features
   - Computes sentiment scores
   - Measures tone variations

2. Calculates mode-specific score: **Tone Consistency** (`referenceTone` set):

   - Compares the output's sentiment with the `referenceTone` sentiment
   - Calculates the absolute sentiment difference
   - Score = max(0, 1 - sentiment\_difference) **Tone Stability** (no `referenceTone`):
   - Analyzes sentiment across the output's sentences
   - Calculates sentiment variance
   - Score = max(0, 1 - sentiment\_variance)

Final score: `mode_specific_score`

### Score interpretation

(0-1)

- 1.0: Perfect tone consistency/stability
- 0.7-0.9: Strong consistency with minor variations
- 0.4-0.6: Moderate consistency with noticeable shifts
- 0.1-0.3: Poor consistency with major tone changes
- 0.0: No consistency - completely different tones

### `preprocessStepResult`

Object with tone metrics:

- **responseSentiment**: Sentiment score for the response (comparison mode).
- **referenceSentiment**: Sentiment score for the `referenceTone` (comparison mode).
- **difference**: Absolute difference between sentiment scores (comparison mode).
- **avgSentiment**: Average sentiment across sentences (stability mode).
- **sentimentVariance**: Variance of sentiment across sentences (stability mode).

## Example

Evaluate whether agent responses match a positive reference tone:

```typescript
import { runEvals } from '@mastra/core/evals'
import { createToneScorer } from '@mastra/evals/scorers/prebuilt'
import { myAgent } from './agent'

const scorer = createToneScorer({
  referenceTone: 'We are happy to help and glad you had a great experience!',
})

const result = await runEvals({
  data: [
    { input: 'How was your experience with our service?' },
    { input: 'Tell me about the customer support' },
  ],
  scorers: [scorer],
  target: myAgent,
  onItemComplete: ({ scorerResults }) => {
    console.log({
      score: scorerResults[scorer.id].score,
    })
  },
})

console.log(result.scores)
```

For more details on `runEvals`, see the [runEvals reference](https://mastra.ai/reference/evals/run-evals).

To add this scorer to an agent, see the [Scorers overview](https://mastra.ai/docs/evals/overview) guide.

## Related

- [Content Similarity Scorer](https://mastra.ai/reference/evals/content-similarity)
- [Toxicity Scorer](https://mastra.ai/reference/evals/toxicity)