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Monte Carlo Simulation Forecasting MCP Server

@bbak

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

bbak

提交者

Bruno Baketarić

关于 Monte Carlo Simulation Forecasting MCP Server

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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Monte Carlo Simulation Forecasting MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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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