Metric MCPs

Metric MCPs

Overview

Metric Model Context Protocols (MCPs) process and analyze specific market metrics across assets and ecosystems. These protocols normalize, transform, and contextualize individual metrics to generate actionable signals.

Available Metric Protocols

Sentiment Analysis MCP

Processes social sentiment data from multiple sources with advanced NLP techniques:

const sentimentMCP = new SentimentAnalysisMCP({
  sources: ['twitter', 'reddit', 'discord', 'telegram'],
  assets: ['BTC', 'ETH', 'SOL', 'AVAX'],
  languages: ['english', 'chinese', 'korean', 'russian'],
  nlpModel: 'advanced'
});

const sentimentScores = await sentimentMCP.process();
// Returns: Multi-dimensional sentiment analysis

Key capabilities:

• Cross-platform sentiment aggregation

• Natural language processing

• Entity recognition and classification

• Sentiment divergence detection

Social Volume MCP

Tracks conversation volume and engagement metrics across social platforms:

const socialVolumeMCP = new SocialVolumeMCP({
  platforms: ['twitter', 'reddit', 'discord', 'telegram'],
  assets: ['BTC', 'ETH', 'SOL'],
  includeBotFiltering: true,
  trackHashtags: true
});

const volumeMetrics = await socialVolumeMCP.process();
// Returns: Social volume metrics with anomaly detection

Key capabilities:

• Cross-platform volume normalization

• Bot activity filtering

• Trend detection and classification

• Organic vs. promotional content differentiation

Engagement MCP

Analyzes quality and depth of social interactions related to crypto assets:

const engagementMCP = new EngagementMCP({
  platforms: ['twitter', 'reddit', 'discord'],
  qualityMetrics: ['reply-depth', 'unique-users', 'content-length'],
  sentimentIntegration: true,
  influencerWeighting: true
});

const engagementMetrics = await engagementMCP.process();
// Returns: Qualitative engagement analysis

Key capabilities:

• Engagement quality assessment

• Influence-weighted metrics

• Community cohesion analysis

• Viral content early detection

Market Dominance MCP

Tracks ecosystem dominance metrics and market share shifts:

const dominanceMCP = new MarketDominanceMCP({
  sectors: ['layer1', 'defi', 'gaming', 'ai'],
  metrics: ['marketcap', 'volume', 'developer-activity'],
  granularity: '1d',
  normalization: 'logarithmic'
});

const dominanceMetrics = await dominanceMCP.process();
// Returns: Dominance metrics with trend analysis

Key capabilities:

• Sector rotation detection

• Dominance trend analysis

• Market share visualization

• Emerging sector identification

Volatility Surface MCP

Analyzes options-derived volatility metrics across term structure:

const volatilitySurfaceMCP = new VolatilitySurfaceMCP({
  assets: ['BTC', 'ETH'],
  expirations: ['7d', '14d', '30d', '90d'],
  strikeRange: [0.5, 2.0],  // Multiple of current price
  interpolationMethod: 'cubic-spline'
});

const volSurface = await volatilitySurfaceMCP.process();
// Returns: Volatility surface metrics and anomalies

Key capabilities:

• Term structure analysis

• Volatility smile assessment

• Option skew interpretation

• Forward-looking risk metrics

Additional Metric MCPs

• Funding Rate MCP: Analyzes perpetual futures funding rates

• Liquidity Depth MCP: Examines order book depth and resilience

• Network Activity MCP: Tracks on-chain transaction metrics

• Developer Commit MCP: Monitors codebase activity metrics

• Implied Volatility MCP: Processes option pricing metrics

Integration Example

// Multi-metric integration
const marketSentimentMCP = new CompositeMetricMCP({
  metrics: [
    new SentimentAnalysisMCP({ /* config */ }),
    new SocialVolumeMCP({ /* config */ }),
    new EngagementMCP({ /* config */ })
  ],
  integrationMethod: 'weighted',
  weights: [0.5, 0.3, 0.2],
  normalizeOutput: true
});

const integratedMetrics = await marketSentimentMCP.process();
// Returns: Integrated metric analysis

This compositional approach enables sophisticated multi-metric analysis through the combination of specialized protocol outputs.