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Monte Carlo Simulation Forecasting Mcp

@bbak

Repository

About Monte Carlo Simulation Forecasting Mcp

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

Basic information

Category

Other

Transports

stdio

Publisher

bbak

Submitted by

Bruno Baketarić

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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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