> For the complete documentation index, see [llms.txt](https://intue-ai.gitbook.io/intue-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://intue-ai.gitbook.io/intue-ai/correlation-mcps.md).

# Correlation MCPs

## Correlation MCPs

### Overview

Correlation Model Context Protocols (MCPs) identify relationships between different data points, assets, and metrics. These specialized protocols detect patterns and connections that individual metric analysis might miss.

### Available Correlation Protocols

#### Cross-Ecosystem Correlation MCP

Analyzes relationships between different market ecosystems (e.g., AI vs. DeFi, Layer-1 vs. Gaming):

```javascript
const crossEcosystemMCP = new CrossEcosystemCorrelationMCP({
  ecosystems: ['ai', 'defi', 'gaming', 'layer1'],
  metrics: ['price', 'volume', 'social'],
  window: '30d',
  method: 'pearson'  // correlation method
});

const correlations = await crossEcosystemMCP.process();
// Returns: correlation matrix between ecosystems
```

Key capabilities:

* Ecosystem rotation detection
* Leading indicator identification
* Correlated market movements
* Divergence recognition

#### Temporal Correlation MCP

Focuses on time-delayed correlations between metrics, identifying leading and lagging relationships:

```javascript
const temporalMCP = new TemporalCorrelationMCP({
  subject: 'BTC-price',
  targets: ['ETH-price', 'SOL-price', 'AVAX-price'],
  maxLag: 72,  // hours
  granularity: '1h',
  significance: 0.95  // statistical significance threshold
});

const lags = await temporalMCP.process();
// Returns: optimal lag time and correlation strength for each target
```

Key capabilities:

* Lead-lag relationship detection
* Price echo identification
* Temporal pattern recognition
* Predictive signal generation

#### Sentiment-Price Correlation MCP

Specializes in linking sentiment metrics to price movements with time-offset calibration:

```javascript
const sentimentPriceMCP = new SentimentPriceCorrelationMCP({
  assets: ['BTC', 'ETH', 'SOL'],
  sentimentSources: ['twitter', 'reddit', 'discord'],
  timeOffset: [-48, 48],  // hours to check before/after
  granularity: '4h'
});

const impacts = await sentimentPriceMCP.process();
// Returns: sentiment impact factors and optimal time offsets
```

Key capabilities:

* Sentiment impact quantification
* Sentiment-price divergence detection
* Time-delayed impact assessment
* Source-specific correlation analysis

#### Volume-Engagement Correlation MCP

Analyzes trading volume in relation to social engagement metrics:

```javascript
const volumeEngagementMCP = new VolumeEngagementCorrelationMCP({
  assets: ['BTC', 'ETH', 'SOL'],
  engagementMetrics: ['tweets', 'reddit-posts', 'discord-messages'],
  anomalyThreshold: 2.5,  // standard deviations
  window: '14d'
});

const anomalies = await volumeEngagementMCP.process();
// Returns: detected anomalies in volume-engagement relationship
```

Key capabilities:

* Volume-engagement divergence detection
* Manipulation pattern recognition
* Organic vs. inorganic activity differentiation
* Anomaly classification

#### Additional Specialized Correlation MCPs

* **Whale Movement Correlation MCP**: Tracks large holder actions across ecosystems
* **Momentum Correlation MCP**: Focuses on rate-of-change correlations between assets
* **Market Dominance Correlation MCP**: Analyzes shifts in market share between related ecosystems
* **Volatility Correlation MCP**: Measures correlation between volatility metrics across assets
* **Developer Activity Correlation MCP**: Links GitHub activity to market metrics
* **Narrative Correlation MCP**: Analyzes keywords and themes across social content

### Integration Example

```javascript
// Combining multiple correlation protocols
const multiCorrelation = new CompositeCorrelationMCP({
  protocols: [
    new SentimentPriceCorrelationMCP({ /* config */ }),
    new TemporalCorrelationMCP({ /* config */ }),
    new WhaleMovementCorrelationMCP({ /* config */ })
  ],
  integrationMethod: 'weighted',
  weights: [0.4, 0.4, 0.2]
});

const integratedSignals = await multiCorrelation.process();
// Returns: integrated correlation signals from multiple protocols
```

This compositional approach enables sophisticated correlation analysis through the combination of specialized protocols.