MoneyWiz MCP Server

"""Pydantic response models for savings and trend analysis.""" from typing import Any from pydantic import BaseModel, Field from typing_extensions import TypedDict class FixedVsVariableData(TypedDict, total=False): """TypedDict for fixed vs variable expense insights.""" fixed_percentage: float variable_percentage: float top_fixed_categories: list[str] optimization_opportunities: list[str] class SpendingPatternsData(TypedDict, total=False): """TypedDict for spending pattern insights.""" trend: str # "increasing", "decreasing", "stable" volatility: float # 0.0-1.0 peak_periods: list[str] recommendations: list[str] patterns_detected: list[str] # Add this field that the service actually provides class CategoryAnalysisData(TypedDict, total=False): """TypedDict for category-based insights.""" highest_impact_categories: list[str] growth_categories: list[str] reduction_opportunities: list[str] concentration: dict[str, Any] # Add this field that the service actually provides class AnalysisPeriodData(TypedDict): """TypedDict for analysis period details.""" start_date: str end_date: str duration_months: int data_quality: str class VisualizationData(TypedDict, total=False): """TypedDict for visualization data.""" chart_type: str data_points: list[dict[str, float]] labels: list[str] line_chart: dict[str, Any] # Add this field that the service actually provides bar_chart: dict[str, Any] # Add this field that the service actually provides class SavingsRecommendation(BaseModel): """Model for a savings recommendation.""" type: str = Field(..., description="Type of recommendation") priority: str = Field(..., description="Priority level (high/medium/low)") priority_score: float | None = Field(None, description="Numeric priority score") title: str = Field(..., description="Recommendation title") description: str = Field(..., description="Detailed description") impact: str = Field(..., description="Expected financial impact") difficulty: str = Field(..., description="Implementation difficulty") category: str | None = Field(None, description="Related category") tips: list[str] | None = Field(None, description="Actionable tips") class CurrentSavingsState(BaseModel): """Model for current savings state.""" savings_rate: float = Field(..., description="Current savings rate percentage") monthly_savings: float = Field(..., description="Average monthly savings amount") total_income: float = Field(..., description="Total income in period") total_expenses: float = Field(..., description="Total expenses in period") class TargetSavingsState(BaseModel): """Model for target savings state.""" target_savings_rate: float = Field( ..., description="Target savings rate percentage" ) projected_savings_rate: float = Field( ..., description="Projected rate with recommendations" ) potential_monthly_savings: float = Field( ..., description="Potential additional monthly savings" ) needed_expense_reduction: float = Field( ..., description="Required expense reduction to meet target" ) class SavingsInsights(BaseModel): """Model for savings insights.""" fixed_vs_variable: FixedVsVariableData = Field( ..., description="Fixed vs variable expense insights" ) spending_patterns: SpendingPatternsData = Field( ..., description="Spending pattern insights" ) category_analysis: CategoryAnalysisData = Field( ..., description="Category-based insights" ) class SavingsOptimizationResponse(BaseModel): """Response model for savings optimization recommendations.""" current_state: CurrentSavingsState = Field( ..., description="Current financial state" ) target_state: TargetSavingsState = Field(..., description="Target financial state") recommendations: list[SavingsRecommendation] = Field( ..., description="Prioritized recommendations" ) insights: SavingsInsights = Field(..., description="Analysis insights") class TrendStatistics(BaseModel): """Model for trend statistics.""" average_monthly: float = Field(..., description="Average monthly amount") median_monthly: float = Field(..., description="Median monthly amount") std_deviation: float = Field(..., description="Standard deviation") trend_direction: str = Field( ..., description="Trend direction (increasing/stable/decreasing)" ) trend_strength: str = Field( ..., description="Trend strength (strong/moderate/weak)" ) growth_rate: float = Field(..., description="Monthly growth rate percentage") class MonthlyTrendData(BaseModel): """Model for monthly trend data.""" month: str = Field(..., description="Month (YYYY-MM format)") total_expenses: float = Field(..., description="Total expenses for the month") transaction_count: int = Field(..., description="Number of transactions") average_transaction: float = Field(..., description="Average transaction amount") class TrendProjection(BaseModel): """Model for trend projections.""" month: str = Field(..., description="Projected month (YYYY-MM format)") projected_amount: float = Field(..., description="Projected amount") confidence: str = Field(..., description="Confidence level (high/medium/low)") class TrendInsight(BaseModel): """Model for trend insights.""" type: str = Field(..., description="Insight type (warning/positive/info)") title: str = Field(..., description="Insight title") description: str = Field(..., description="Detailed description") priority: str = Field(..., description="Priority level") class SpendingTrendResponse(BaseModel): """Response model for spending trend analysis.""" period: AnalysisPeriodData = Field(..., description="Analysis period details") monthly_data: list[MonthlyTrendData] = Field(..., description="Monthly breakdown") statistics: TrendStatistics = Field(..., description="Trend statistics") insights: list[TrendInsight] = Field(..., description="Trend insights") projections: list[TrendProjection] = Field(..., description="Future projections") visualizations: VisualizationData = Field(..., description="Visualization data") class CategoryTrend(BaseModel): """Model for category trend data.""" category: str = Field(..., description="Category name") total_spent: float = Field(..., description="Total spent in period") percentage_of_total: float = Field(..., description="Percentage of total expenses") trend: str = Field(..., description="Trend direction") growth_rate: float = Field(..., description="Growth rate percentage") monthly_average: float = Field(..., description="Monthly average") insights: list[TrendInsight] = Field(..., description="Category-specific insights") class CategoryTrendsResponse(BaseModel): """Response model for category trends analysis.""" period: AnalysisPeriodData = Field(..., description="Analysis period details") category_trends: list[CategoryTrend] = Field(..., description="Trends by category") overall_insights: list[TrendInsight] = Field(..., description="Overall insights") class IncomeExpenseTrend(BaseModel): """Model for income/expense trend data.""" direction: str = Field(..., description="Trend direction") growth_rate: float = Field(..., description="Growth rate percentage") stability: str = Field(..., description="Stability level") average: float | None = Field(None, description="Average value") improving: bool | None = Field(None, description="Whether trend is improving") class MonthlyIncomeExpenseData(BaseModel): """Model for monthly income vs expense data.""" month: str = Field(..., description="Month (YYYY-MM format)") income: float = Field(..., description="Total income") expenses: float = Field(..., description="Total expenses") net_savings: float = Field(..., description="Net savings") savings_rate: float = Field(..., description="Savings rate percentage") class IncomeVsExpenseTrendsResponse(BaseModel): """Response model for income vs expense trends.""" period: AnalysisPeriodData = Field(..., description="Analysis period") monthly_data: list[MonthlyIncomeExpenseData] = Field( ..., description="Monthly breakdown" ) trends: dict[str, IncomeExpenseTrend] = Field( ..., description="Trends for income, expenses, savings" ) insights: list[TrendInsight] = Field(..., description="Analysis insights")