@spaik/mcp-server-roi/dist/schemas/projection.js

MCP server for AI ROI prediction and tracking with Monte Carlo simulations

import { z } from 'zod';
export const FinancialMetricsSchema = z.object({
    monthly_cost_savings: z.number(),
    monthly_time_savings_hours: z.number(),
    quality_improvement_value: z.number(),
    revenue_uplift: z.number(),
    total_monthly_benefit: z.number()
});
export const ROICalculationsSchema = z.object({
    total_investment: z.number(),
    net_present_value: z.number(),
    internal_rate_of_return: z.number(),
    payback_period_months: z.number(),
    five_year_roi: z.number(),
    break_even_date: z.string().datetime()
});
export const ProjectionSchema = z.object({
    id: z.string().uuid().optional(),
    project_id: z.string().uuid(),
    scenario_name: z.string().default('Base Case'),
    metadata: z.object({
        confidence_level: z.number().min(0).max(1).default(0.95),
        assumptions: z.array(z.object({
            category: z.string(),
            description: z.string(),
            impact: z.enum(['low', 'medium', 'high'])
        })).default([])
    }),
    implementation_costs: z.object({
        software_licenses: z.number().min(0),
        development_hours: z.number().min(0),
        training_costs: z.number().min(0),
        infrastructure: z.number().min(0),
        ongoing_monthly: z.number().min(0)
    }),
    timeline_months: z.number().min(1),
    financial_metrics: z.object({
        conservative: FinancialMetricsSchema,
        expected: FinancialMetricsSchema,
        optimistic: FinancialMetricsSchema
    }),
    calculations: ROICalculationsSchema,
    created_at: z.string().datetime().optional(),
    updated_at: z.string().datetime().optional()
});
export const ProjectionCreateSchema = ProjectionSchema.omit({
    id: true,
    created_at: true,
    updated_at: true
});
// Monte Carlo simulation results
export const MonteCarloResultsSchema = z.object({
    projection_id: z.string().uuid(),
    simulation_count: z.number(),
    run_date: z.string().datetime(),
    roi_distribution: z.object({
        percentiles: z.object({
            p5: z.number(),
            p25: z.number(),
            p50: z.number(),
            p75: z.number(),
            p95: z.number()
        }),
        mean: z.number(),
        std_dev: z.number(),
        confidence_interval_95: z.tuple([z.number(), z.number()])
    }),
    payback_distribution: z.object({
        percentiles: z.record(z.string(), z.number()),
        probability_within_12_months: z.number(),
        probability_within_24_months: z.number()
    }),
    risk_analysis: z.object({
        probability_of_loss: z.number(),
        value_at_risk_95: z.number(),
        key_risk_drivers: z.array(z.object({
            factor: z.string(),
            impact_percentage: z.number(),
            correlation: z.number()
        }))
    })
});
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