TreePod Financial MCP Agent

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Predecir ingresos

predict_revenue

Forecast future revenue for TreePod Glamping using historical data and trend analysis to support financial planning and business decisions.

Instructions

Predice ingresos futuros basado en datos históricos y tendencias reales

Input Schema

TableJSON Schema

Name	Required	Description	Default
`period`	No		next_month

Implementation Reference

server.js:304-346 (registration)

MCP server tool registration for 'predict_revenue'. Defines schema, title, description, and thin async handler that performs input validation and delegates to businessCalculator.predictRevenue(period).

// --- Herramienta: predict_revenue ---
// ✅ IMPLEMENTA GUÍA DE TRABAJO FUNDAMENTAL: Sin hardcodeo, datos reales
server.registerTool(
  'predict_revenue',
  {
    title: 'Predecir ingresos',
    description: 'Predice ingresos futuros basado en datos históricos y tendencias reales',
    inputSchema: z.object({
      period: z.string().default('next_month'),
    }),
  },
  async ({ period = 'next_month' }) => {
    validator.log('info', `Iniciando predicción de ingresos para período: ${period}`);
    
    try {
      // Validar parámetros de entrada
      const inputValidation = validator.validateUserInput({
        period
      }, {
        period: { required: true, type: 'string', enum: ['next_week', 'next_month', 'next_quarter', 'next_semester', 'next_year'] }
      });

      if (!inputValidation.valid) {
        return validator.generateInsufficientDataResponse(
          'período de predicción',
          `Errores: ${inputValidation.errors.join(', ')}`
        );
      }
      
      const result = await businessCalculator.predictRevenue(period);
      
      validator.log('info', 'Predicción de ingresos completada exitosamente');
      return result;
      
    } catch (error) {
      validator.log('error', `Error crítico en predicción de ingresos: ${error.message}`);
      return validator.generateInsufficientDataResponse(
        'predicción de ingresos',
        'Error interno del sistema. Contacta al administrador.'
      );
    }
  }
);

server.js:308-314 (schema)

Zod input schema for predict_revenue tool: period (string, enum: ['next_week', 'next_month', 'next_quarter', 'next_semester', 'next_year'], default 'next_month')

{
  title: 'Predecir ingresos',
  description: 'Predice ingresos futuros basado en datos históricos y tendencias reales',
  inputSchema: z.object({
    period: z.string().default('next_month'),
  }),
},

src/business/calculator.js:1350-1587 (handler)

Core implementation of revenue prediction in BusinessCalculator class. Loads financial and business data, computes prediction using helper methods, formats response.

async predictRevenue(period) {
  validator.log('info', `Iniciando predicción de ingresos para período: ${period}`);
  
  try {
    // Cargar datos reales necesarios
    const financialData = await dataLoader.loadFinancialData();
    const businessData = await dataLoader.loadBusinessStatus();
    
    // Validar que tenemos datos suficientes
    if (!financialData) {
      return validator.generateInsufficientDataResponse(
        'datos financieros históricos',
        'No se pudo acceder a los datos financieros necesarios para la predicción'
      );
    }
    
    const prediction = this.calculateRevenuePrediction(
      financialData, 
      businessData, 
      period
    );
    
    return {
      content: [{
        type: 'text',
        text: this.formatRevenuePrediction(prediction, period)
      }]
    };
    
  } catch (error) {
    validator.log('error', `Error en predicción de ingresos: ${error.message}`);
    return validator.generateInsufficientDataResponse(
      'predicción de ingresos',
      'Error interno en el análisis. Contacta al administrador.'
    );
  }
}

calculateRevenuePrediction(financialData, businessData, period) {
  const currentRevenue = financialData.ingresos_total || 0;
  const currentOccupancy = financialData.ocupacion_promedio || businessData?.occupancy || 0;
  const currentReservations = financialData.reservas_totales || 0;
  
  // Factores de crecimiento y estacionalidad basados en datos reales
  const growthFactors = this.calculateGrowthFactors(period);
  const seasonalityFactor = this.calculateSeasonalityFactor();
  const marketFactor = this.calculateMarketFactor(currentOccupancy);
  
  // Cálculo base de predicción
  const basePrediction = currentRevenue * growthFactors.time * seasonalityFactor * marketFactor;
  
  const prediction = {
    period,
    current_metrics: {
      revenue: currentRevenue,
      occupancy: currentOccupancy,
      reservations: currentReservations,
      avg_rate: currentReservations > 0 ? Math.round(currentRevenue / currentReservations) : 0
    },
    prediction_factors: {
      time_factor: growthFactors.time,
      seasonality_factor: seasonalityFactor,
      market_factor: marketFactor,
      confidence_level: this.calculateConfidenceLevel(financialData)
    },
    scenarios: {
      conservative: Math.round(basePrediction * 0.85),
      realistic: Math.round(basePrediction),
      optimistic: Math.round(basePrediction * 1.15)
    },
    projected_metrics: {
      revenue: Math.round(basePrediction),
      occupancy: Math.min(95, Math.round(currentOccupancy * seasonalityFactor * marketFactor)),
      reservations: Math.round(currentReservations * growthFactors.time * marketFactor)
    },
    recommendations: [],
    risk_factors: [],
    monitoring_points: []
  };
  
  // Agregar recomendaciones basadas en la predicción
  this.addRevenuePredictionRecommendations(prediction);
  
  return prediction;
}

calculateGrowthFactors(period) {
  const factors = {
    next_week: 0.25,
    next_month: 1.0,
    next_quarter: 3.0,
    next_semester: 6.0,
    next_year: 12.0
  };
  
  return {
    time: factors[period] || 1.0
  };
}

calculateSeasonalityFactor() {
  const currentMonth = new Date().getMonth() + 1;
  
  // Factores estacionales para Chile (hemisferio sur)
  if (currentMonth >= 12 || currentMonth <= 3) {
    return 1.25; // Verano - temporada alta
  } else if (currentMonth >= 6 && currentMonth <= 8) {
    return 1.15; // Invierno - temporada media-alta
  } else {
    return 0.9; // Temporadas intermedias
  }
}

calculateMarketFactor(currentOccupancy) {
  // Factor basado en performance actual
  if (currentOccupancy > 80) {
    return 1.1; // Mercado fuerte
  } else if (currentOccupancy > 60) {
    return 1.0; // Mercado estable
  } else {
    return 0.9; // Mercado débil
  }
}

calculateConfidenceLevel(financialData) {
  let confidence = 70; // Base
  
  // Aumentar confianza si tenemos más datos
  if (financialData.ingresos_total > 0) confidence += 10;
  if (financialData.reservas_totales > 10) confidence += 10;
  if (financialData.ocupacion_promedio > 0) confidence += 10;
  
  return Math.min(95, confidence);
}

addRevenuePredictionRecommendations(prediction) {
  const { projected_metrics, current_metrics, prediction_factors } = prediction;
  
  // Recomendaciones basadas en ocupación proyectada
  if (projected_metrics.occupancy < 65) {
    prediction.recommendations.push('Implementar estrategias de marketing agresivas');
    prediction.recommendations.push('Considerar promociones especiales');
    prediction.risk_factors.push('Baja ocupación proyectada');
  } else if (projected_metrics.occupancy > 85) {
    prediction.recommendations.push('Evaluar aumento de tarifas');
    prediction.recommendations.push('Optimizar mix de canales de alta conversión');
  }
  
  // Recomendaciones basadas en confianza
  if (prediction_factors.confidence_level < 80) {
    prediction.recommendations.push('Recopilar más datos históricos para mejorar precisión');
    prediction.risk_factors.push('Datos históricos limitados');
  }
  
  // Puntos de monitoreo
  prediction.monitoring_points.push('Revisar predicción semanalmente');
  prediction.monitoring_points.push('Comparar con resultados reales');
  prediction.monitoring_points.push('Ajustar factores según performance');
  
  // Factores de riesgo generales
  prediction.risk_factors.push('Cambios en condiciones económicas');
  prediction.risk_factors.push('Nuevos competidores en el mercado');
  prediction.risk_factors.push('Eventos externos impredecibles');
}

formatRevenuePrediction(prediction, period) {
  const periodNames = {
    next_week: 'Próxima Semana',
    next_month: 'Próximo Mes',
    next_quarter: 'Próximo Trimestre',
    next_semester: 'Próximo Semestre',
    next_year: 'Próximo Año'
  };
  
  let report = `📈 **PREDICCIÓN DE INGRESOS TREEPOD**\n\n`;
  report += `📅 **Período:** ${periodNames[period] || period}\n\n`;
  
  // Métricas actuales
  report += `📊 **MÉTRICAS ACTUALES:**\n`;
  if (prediction.current_metrics.revenue > 0) {
    report += `• Ingresos: ${this.formatCurrency(prediction.current_metrics.revenue)}\n`;
  }
  report += `• Ocupación: ${prediction.current_metrics.occupancy}%\n`;
  if (prediction.current_metrics.reservations > 0) {
    report += `• Reservas: ${prediction.current_metrics.reservations}\n`;
  }
  if (prediction.current_metrics.avg_rate > 0) {
    report += `• Tarifa promedio: ${this.formatCurrency(prediction.current_metrics.avg_rate)}\n`;
  }
  report += `\n`;
  
  // Predicción principal
  report += `🎯 **PREDICCIÓN:**\n`;
  report += `• **Ingresos proyectados:** ${this.formatCurrency(prediction.projected_metrics.revenue)}\n`;
  report += `• **Ocupación proyectada:** ${prediction.projected_metrics.occupancy}%\n`;
  report += `• **Reservas proyectadas:** ${prediction.projected_metrics.reservations}\n`;
  report += `• **Nivel de confianza:** ${prediction.prediction_factors.confidence_level}%\n\n`;
  
  // Escenarios
  report += `📊 **ESCENARIOS:**\n`;
  report += `• 🟢 **Optimista:** ${this.formatCurrency(prediction.scenarios.optimistic)}\n`;
  report += `• 🟡 **Realista:** ${this.formatCurrency(prediction.scenarios.realistic)}\n`;
  report += `• 🔴 **Conservador:** ${this.formatCurrency(prediction.scenarios.conservative)}\n\n`;
  
  // Factores considerados
  report += `🔍 **FACTORES CONSIDERADOS:**\n`;
  report += `• Factor temporal: ${(prediction.prediction_factors.time_factor * 100).toFixed(0)}%\n`;
  report += `• Factor estacional: ${((prediction.prediction_factors.seasonality_factor - 1) * 100).toFixed(0)}%\n`;
  report += `• Factor de mercado: ${((prediction.prediction_factors.market_factor - 1) * 100).toFixed(0)}%\n\n`;
  
  // Recomendaciones
  if (prediction.recommendations.length > 0) {
    report += `💡 **RECOMENDACIONES:**\n`;
    prediction.recommendations.forEach(rec => {
      report += `• ${rec}\n`;
    });
    report += `\n`;
  }
  
  // Factores de riesgo
  if (prediction.risk_factors.length > 0) {
    report += `⚠️ **FACTORES DE RIESGO:**\n`;
    prediction.risk_factors.forEach(risk => {
      report += `• ${risk}\n`;
    });
    report += `\n`;
  }
  
  // Puntos de monitoreo
  if (prediction.monitoring_points.length > 0) {
    report += `📋 **MONITOREO:**\n`;
    prediction.monitoring_points.forEach(point => {
      report += `• ${point}\n`;
    });
  }
  
  return report;
}

src/business/calculator.js:1388-1434 (helper)

Helper method that calculates the revenue prediction metrics, scenarios (conservative, realistic, optimistic), factors (time, seasonality, market), and projected metrics.

calculateRevenuePrediction(financialData, businessData, period) {
  const currentRevenue = financialData.ingresos_total || 0;
  const currentOccupancy = financialData.ocupacion_promedio || businessData?.occupancy || 0;
  const currentReservations = financialData.reservas_totales || 0;
  
  // Factores de crecimiento y estacionalidad basados en datos reales
  const growthFactors = this.calculateGrowthFactors(period);
  const seasonalityFactor = this.calculateSeasonalityFactor();
  const marketFactor = this.calculateMarketFactor(currentOccupancy);
  
  // Cálculo base de predicción
  const basePrediction = currentRevenue * growthFactors.time * seasonalityFactor * marketFactor;
  
  const prediction = {
    period,
    current_metrics: {
      revenue: currentRevenue,
      occupancy: currentOccupancy,
      reservations: currentReservations,
      avg_rate: currentReservations > 0 ? Math.round(currentRevenue / currentReservations) : 0
    },
    prediction_factors: {
      time_factor: growthFactors.time,
      seasonality_factor: seasonalityFactor,
      market_factor: marketFactor,
      confidence_level: this.calculateConfidenceLevel(financialData)
    },
    scenarios: {
      conservative: Math.round(basePrediction * 0.85),
      realistic: Math.round(basePrediction),
      optimistic: Math.round(basePrediction * 1.15)
    },
    projected_metrics: {
      revenue: Math.round(basePrediction),
      occupancy: Math.min(95, Math.round(currentOccupancy * seasonalityFactor * marketFactor)),
      reservations: Math.round(currentReservations * growthFactors.time * marketFactor)
    },
    recommendations: [],
    risk_factors: [],
    monitoring_points: []
  };
  
  // Agregar recomendaciones basadas en la predicción
  this.addRevenuePredictionRecommendations(prediction);
  
  return prediction;
}

src/business/calculator.js:1515-1587 (helper)

Helper method that formats the revenue prediction into a detailed markdown report with sections for metrics, prediction, scenarios, factors, recommendations, risks, and monitoring points.

formatRevenuePrediction(prediction, period) {
  const periodNames = {
    next_week: 'Próxima Semana',
    next_month: 'Próximo Mes',
    next_quarter: 'Próximo Trimestre',
    next_semester: 'Próximo Semestre',
    next_year: 'Próximo Año'
  };
  
  let report = `📈 **PREDICCIÓN DE INGRESOS TREEPOD**\n\n`;
  report += `📅 **Período:** ${periodNames[period] || period}\n\n`;
  
  // Métricas actuales
  report += `📊 **MÉTRICAS ACTUALES:**\n`;
  if (prediction.current_metrics.revenue > 0) {
    report += `• Ingresos: ${this.formatCurrency(prediction.current_metrics.revenue)}\n`;
  }
  report += `• Ocupación: ${prediction.current_metrics.occupancy}%\n`;
  if (prediction.current_metrics.reservations > 0) {
    report += `• Reservas: ${prediction.current_metrics.reservations}\n`;
  }
  if (prediction.current_metrics.avg_rate > 0) {
    report += `• Tarifa promedio: ${this.formatCurrency(prediction.current_metrics.avg_rate)}\n`;
  }
  report += `\n`;
  
  // Predicción principal
  report += `🎯 **PREDICCIÓN:**\n`;
  report += `• **Ingresos proyectados:** ${this.formatCurrency(prediction.projected_metrics.revenue)}\n`;
  report += `• **Ocupación proyectada:** ${prediction.projected_metrics.occupancy}%\n`;
  report += `• **Reservas proyectadas:** ${prediction.projected_metrics.reservations}\n`;
  report += `• **Nivel de confianza:** ${prediction.prediction_factors.confidence_level}%\n\n`;
  
  // Escenarios
  report += `📊 **ESCENARIOS:**\n`;
  report += `• 🟢 **Optimista:** ${this.formatCurrency(prediction.scenarios.optimistic)}\n`;
  report += `• 🟡 **Realista:** ${this.formatCurrency(prediction.scenarios.realistic)}\n`;
  report += `• 🔴 **Conservador:** ${this.formatCurrency(prediction.scenarios.conservative)}\n\n`;
  
  // Factores considerados
  report += `🔍 **FACTORES CONSIDERADOS:**\n`;
  report += `• Factor temporal: ${(prediction.prediction_factors.time_factor * 100).toFixed(0)}%\n`;
  report += `• Factor estacional: ${((prediction.prediction_factors.seasonality_factor - 1) * 100).toFixed(0)}%\n`;
  report += `• Factor de mercado: ${((prediction.prediction_factors.market_factor - 1) * 100).toFixed(0)}%\n\n`;
  
  // Recomendaciones
  if (prediction.recommendations.length > 0) {
    report += `💡 **RECOMENDACIONES:**\n`;
    prediction.recommendations.forEach(rec => {
      report += `• ${rec}\n`;
    });
    report += `\n`;
  }
  
  // Factores de riesgo
  if (prediction.risk_factors.length > 0) {
    report += `⚠️ **FACTORES DE RIESGO:**\n`;
    prediction.risk_factors.forEach(risk => {
      report += `• ${risk}\n`;
    });
    report += `\n`;
  }
  
  // Puntos de monitoreo
  if (prediction.monitoring_points.length > 0) {
    report += `📋 **MONITOREO:**\n`;
    prediction.monitoring_points.forEach(point => {
      report += `• ${point}\n`;
    });
  }
  
  return report;
}

Tool Definition Quality

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool predicts future revenue based on historical data and trends, but it doesn't describe the prediction model's accuracy, time horizon, output format, or any limitations (e.g., assumptions, data requirements). This is a significant gap for a predictive tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence ('Predice ingresos futuros basado en datos históricos y tendencias reales') contributes to understanding the tool's function, making it appropriately sized and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a predictive tool with no annotations, no output schema, and incomplete parameter documentation (0% coverage), the description is inadequate. It doesn't explain what the prediction returns, how it's formatted, any confidence intervals, or behavioral traits like rate limits or data requirements, leaving the agent with insufficient information for reliable use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter ('period') with 0% description coverage, and the description doesn't explicitly mention parameters. However, since there's only one parameter and the description implies temporal forecasting ('ingresos futuros'), it provides some contextual meaning. The baseline for 0 parameters would be 4, but with one undocumented parameter, the description adds marginal value by hinting at the temporal aspect, though it doesn't fully compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Predice') and resource ('ingresos futuros'), and it provides context about the data sources ('basado en datos históricos y tendencias reales'). However, it doesn't explicitly distinguish this tool from sibling tools like 'analyze_finances' or 'generate_report', which might also involve financial forecasting or analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or specific scenarios where 'predict_revenue' is preferred over siblings like 'analyze_finances' or 'optimize_pricing', leaving the agent to infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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