bbak/mcs-mcp

Provide Monte-Carlo-Simulation and Flow Data diagnostics to AI Agents

39
/ 100
Emerging

This project helps project managers and team leads predict when work will be completed and analyze their delivery process by connecting AI assistants like Claude to Jira project history. It takes your team's historical Jira data, like issue keys, types, status transitions, and timestamps, and produces natural-language answers and interactive charts about forecasting, bottlenecks, and process predictability. It is designed for anyone managing projects or teams in Jira who wants data-driven insights instead of relying on gut feelings.

Use this if you manage projects in Jira and want to use an AI assistant to get reliable, data-driven forecasts for task completion dates or to identify bottlenecks and improve your team's delivery process.

Not ideal if your Jira board mixes multiple workflows with different statuses or orders, or if you need to analyze projects that span across multiple boards with complex JQL queries.

project-management delivery-analytics jira-analysis flow-metrics probabilistic-forecasting
No Package No Dependents
Maintenance 10 / 25
Adoption 5 / 25
Maturity 11 / 25
Community 13 / 25

How are scores calculated?

Stars

9

Forks

2

Language

Go

License

Apache-2.0

Category

go-mcp-servers

Last pushed

Mar 12, 2026

Commits (30d)

0

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