MCP For Claude Marketplace
Demand Forecast Calculator

Demand Forecast Calculator MCP Connector for Claude

A+

Generate 3-month demand projections using SMA, WMA, and Exponential Smoothing methods.

3 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides advanced forecasting capabilities to predict future demand based on historical data. It implements three distinct mathematical models: Simple Moving Average (analyze_sma), Weighted Moving Average (analyze_wma), and Exponential Smoothing (analyze_exponential_smoothing). For each method, the tools perform error backtesting to provide Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), allowing you to identify the most accurate model for your specific dataset. Use these tools in Cursor, VS Code, Claude Desktop, or Windsurf to automate demand planning.

demand-planningsmawmaexponential-smoothingforecasting-engine

3 tools expose this connector's capabilities to your AI agent.

analyze_exponential_smoothing

Calculate demand forecast using Exponential Smoothing

analyze_sma

Calculate demand forecast using Simple Moving Average (SMA)

analyze_wma

Calculate demand forecast using Weighted Moving Average (WMA)

See how to talk to your AI agent using Demand Forecast Calculator.

Calculate a 3-month forecast using SMA with a window size of 3 for this demand: [10, 12, 15, 14, 18]

The `analyze_sma` tool has processed your data. The forecast for the next three months is [15.67, 15.67, 15.67] with a calculated MAPE of approximately 8.2%.

Use Exponential Smoothing to predict demand for [100, 110, 120] with alpha=0.5 and beta=0.3.

Using `analyze_exponential_smoothing`, the predicted demand for the next three periods is [130, 140, 150].

Analyze demand using WMA with weights [0.5, 0.3, 0.2] for data: [50, 60, 70]

The `analyze_wma` tool calculated the forecast as [76, 76, 76] based on your provided weights and historical sequence.

The server supports Simple Moving Average (`analyze_sma`), Weighted Moving Average (`analyze_wma`), and Exponential Smoothing (`analyze_exponential_smoothing`).

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