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Monte Carlo Simulation Forecasting MCP Server

@bbak

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基本情報

カテゴリ

その他

トランスポート

stdio

作者

bbak

投稿者

Bruno Baketarić

Monte Carlo Simulation Forecasting MCP Server について

Monte-Carlo-Simulation Forecasting and various statistical methods to assess the stability of the flow and diagnose flow issues.

#forecasting#monte-carlo-simulation#xmr-charts#atlassian-jira#wip-age#walk-forward-backtesting

Monte Carlo Simulation Forecasting MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcs-mcp": {
      "command": "/path/to/server/mcs-mcp.exe",
      "args": []
    }
  }
}

Monte Carlo Simulation Forecasting MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Monte Carlo Simulation Forecasting Mcp?

Monte Carlo Simulation Forecasting Mcp (MCS-MCP) is a Model Context Protocol server that provides AI assistants with probabilistic forecasting and analysis for software delivery projects. It uses historical Jira data and high-performance Monte Carlo simulations to generate actionable, percentile-based delivery insights, with a focus on mathematical hardening and security-by-design.

How to use Monte Carlo Simulation Forecasting Mcp?

Download or build the binary, configure Jira authentication in a .env file, and point your MCP client to the binary. Once connected, ask the AI agent to look at a project and board, discover its workflow, and request forecasts or the analytical roadmap. Supported authentication methods include Personal Access Tokens and session cookies.

Key features of Monte Carlo Simulation Forecasting Mcp

  • Stratified analytics with type-aware capacity clash detection.
  • Monte Carlo forecasting with 10,000+ simulations for duration/scope.
  • Walk-forward backtesting to validate forecast accuracy.
  • XmR control charts and stability indices for special cause detection.
  • Workflow semantic discovery to identify true bottlenecks.
  • Process yield and abandonment quantification by work type.

Use cases of Monte Carlo Simulation Forecasting Mcp

  • Forecasting project completion dates with probabilistic confidence intervals.
  • Determining optimal sprint scope based on historical throughput.
  • Detecting capacity clashes where bug work consumes team capacity.
  • Identifying status-level bottlenecks via workflow

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