MCS-MCP: Monte-Carlo Simulation for Model Context Protocol

MCS-MCP is a sophisticated Model Context Protocol (MCP) server that empowers AI assistants with deep analytical and forecasting capabilities for software delivery projects. By leveraging historical Jira data and high-performance Monte-Carlo simulations, it transforms raw project history into actionable, probabilistic delivery insights with a strong focus on mathematical hardening and defensive design.

🚀 Key Capabilities

• Stratified Analytics Baseline: Type-stratification is pervasive across the suite. Detect "Capacity Clashes" (Bug-Tax) in simulations, identify type-specific bottlenecks in Status Residency, and assess WIP Age using type-aware benchmarks.
• Monte-Carlo Forecasting: Run 10,000+ simulations to answer "When will it be done?" (Duration) or "How much can we do?" (Scope). Automatically coordinates sampling across multiple work types to ensure realistic theoretical capacity.
• Forecast Backtesting: Perform Walk-Forward Analysis to empirically validate forecast accuracy by "time-travelling" into historical data.
• Predictability Guardrails: Use XmR Control Charts and Stability Indices (stratifiable by type) to detect "Special Cause" variation and assess process stability.
• Workflow Semantic Discovery: Automatically infer the roles of workflow statuses (Active, Queue, Demand, Finished) to identify true bottlenecks instead of administrative delays.
• Process Yield & Abandonment: Quantify "waste" by identifying exactly where work (broken down by type) is discarded in the discovery or execution pipeline.
• High-Fidelity Aging Analysis: Track WIP Age and status-level persistence to identify "neglected" inventory before it impacts delivery.
• Strategic Evolution Tracking: Perform longitudinal audits using Three-Way Control Charts (Weekly/Monthly) to detect systemic improvements or process drift over time.
• Guided Analytical Roadmaps: Proactively guide AI agents through the correct sequence of diagnostic steps (Stability -> Discovery -> Analysis) based on specific goals.

🛡️ Data Security & GRC Principles

Work-Management Systems like Atlassian Jira often contain sensitive project and personal data. MCS-MCP is built with a Security-by-Design approach, operating on two fundamental governing principles:

1. The "Need-to-Know" Principle (Data Minimization)

To protect intellectual property and privacy, the server strictly minimizes the data it ingests and persists.

• What we ingest & persist: Analytical metadata only—Issue Keys, Issue Types, Status Transitions, Timestamps, and Resolution names. This is the minimum set required for high-fidelity flow analysis.
• What we DROP: While the Jira API might return comprehensive issue objects, the system is designed to immediately drop sensitive content such as Titles, Descriptions, Acceptance Criteria, or Assignees.

This ensures that even if the server's cache were compromised, it contains no human-readable content that could leak project secrets or PII. Furthermore, because this data is never processed by the analytical engine or stored in memory, it is impossible for sensitive content to leak to the AI Agent during interaction.

2. The Transparency Principle (Auditability)

We believe in "No Black Boxes." The server operates primarily from its local caches after the initial ingestion.

• Human-Readable Caches: All persisted data (Event Logs, Workflow Mappings) is stored in standard, human-readable formats (JSON and JSON-Lines) in the data directory.
• Verifiable Logic: You can scan or monitor these files at any time to verify that no sensitive data has leaked into the server's long-term memory.

🛠️ How it Works

MCS-MCP operates on the principle of Data-Driven Probabilism. It avoids single-point averages, which often mask risk, and instead provides Percentile-based outcomes (e.g., P85 "Likely" confidence).

1. Ingestion: The server fetches full Jira changelogs via a centralized ingestion layer, using Backward Boundary Scanning to reconstruct project-local history while calculating exact residency time (in seconds) for every item across every status.

2. Context Resolution: Statuses are mapped to a meta-workflow (Demand → Upstream → Downstream → Finished) to ensure the simulation "clock" reflects actual value consumption.

3. Simulation & Validation: The engine simulates potential futures and optionally validates them via walk-forward backtesting to ensure historical reliability.

4. Diagnostic Guidance: An AI-orchestrated Roadmap tool guides agents through a sequence of diagnostic steps.

⚠️ Probabilistic Nature & Disclaimer

MCS-MCP is a statistical tool. It generates probabilistic forecasts based on historical performance, not guarantees.

• No Direct Answer: A forecast saying "85% confidence by Oct 12" means there is a 15% chance it will take longer.
• Garbage In, Garbage Out: Results are strictly dependent on the quality and consistency of your Jira data.
• No Liability: This tool is provided "AS IS". The authors and contributors are not responsible for any project delays, financial losses, or business decisions made based on its output.

🏃 Getting Started

Prerequisites

• Access to Atlassian Jira (Data Center or Cloud)
• A MCP-capable AI Agent to chat with
• Recent Version of Go if you want to build yourself

Mini-How-To

• Build or download a release
• Configure the server via .env
• Configure a AI Agent to use it as an MCP tool
• Chat:
  • Ask the Agent to look a Project and then a Board
  • Ask the Agent to discover the workflow
  • Ask the Agent for what the MCP-Server can do or the analytical roadmap

Authentication

The server supports both Personal Access Tokens (PAT) and session-based (cookie) authentication.

Option A: Personal Access Token (PAT) - Preferred Configure your Jira PAT in the .env file (example file included):

JIRA_TOKEN=your-personal-access-token

Option B: Session Cookies - Fallback If PAT is not available, provide session cookies extracted from an active browser:

• JIRA_SESSION_ID: Your Jira session ID.
• JIRA_XSRF_TOKEN: Your XSRF token.
• JIRA_REMEMBERME_COOKIE: Your Jira RememberMe cookie. (Optional, but recommended for long-running sessions)
• (optional) JIRA_GCILB, JIRA_GCLB: Actually these are Google-Cloud Load Balancer Cookies.

Building from Sources

On Windows (PowerShell):

.\build.ps1 build

On Unix/Linux (Make):

Untested, but should work.

make build

The resulting binary will be located in the dist/ folder (e.g., dist/mcs-mcp.exe) along with a exemplary .env file.

Configuring as an MCP Tool

To use as a server for an AI Agent (like Claude or Gemini), point your MCP client configuration to the compiled binary:

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

Make sure that the Server can write to this directory to create cache and logs folders - or reconfigure using DATA_PATH.

📖 Guided Interaction

MCS-MCP is designed to be used by AI Agents as a "Technical Co-Pilot". For detailed guidance on specific workflows, refer to:

• Project Charter: Conceptual foundations and architectural principles.
• Interaction Use Cases: Detailed scenarios for PMs and AI Agents (When, Scope, Bottlenecks, Backtesting, etc.).
• Architecture Deep-Dive: Aging math, backflow policies, and the status-granular flow model.
• Testing & Verification: Instructions for the MCSTEST sandbox and mock data generator.

⚖️ Conceptual Integrity

This project adheres to the core principles of Cohesion, Coherence, and Consistency. Every tool and analytical model is designed to provide a unified, reliable view of delivery performance without administrative noise.

📜 License

This project is licensed under the Apache License 2.0. See the LICENSE and NOTICE files for details.

Copyright © 2026 Bruno Baketarić.