Go-based Model Context Protocol (MCP) server that lets AI assistants (e.g. Claude) perform full-featured Jira operations such as issue creation, sprint management and workflow transitions.
https://github.com/nguyenvanduocit/jira-mcpIf you're tired of alt-tabbing to Jira every time you need to create tickets, update sprint planning, or check issue status, this MCP server changes everything. Instead of breaking your flow to click through Jira's web interface, you can handle all your project management tasks directly through Claude or any MCP-compatible AI assistant.
You know the drill: you're deep in code, discover a bug, and now you need to create a ticket. But first you have to:
By the time you're back to your editor, you've lost your mental context. This MCP server eliminates that entire workflow disruption.
While Atlassian offers an official MCP connector, this implementation focuses on the operations you actually need day-to-day. Instead of basic API wrappers, you get 20+ specialized tools designed around real development workflows:
Sprint Management Made Simple
Issue Operations That Actually Work
Time Tracking Without the Overhead
Setup takes under 5 minutes. The server runs as a Docker container or standalone binary, and connects to your existing Jira instance using standard API tokens. Once configured in Cursor or your preferred AI assistant, you can:
"Create a bug ticket for the login timeout issue and assign it to the current sprint"
"Show me all tickets assigned to me that are ready for testing"
"Move tickets PROJ-123, PROJ-124, and PROJ-125 to the next sprint"
"Add 2 hours of work to PROJ-456 and comment that the API integration is complete"
The AI understands your Jira context - projects, sprints, issue types, and workflow states - so these requests work immediately without additional configuration.
Daily Standup Prep: Ask "What did I work on yesterday?" and get a formatted list of your recent ticket activity, comments, and time entries.
Sprint Planning: "Show me all unassigned tickets in the backlog" or "What's the current sprint capacity?" Get the data you need without opening Jira boards.
Bug Triage: Create tickets directly from error logs or user reports. The AI can parse context and suggest appropriate fields, labels, and assignees.
Code Review Integration: When you spot issues during review, create tickets and link them to the relevant commits or pull requests without leaving your development environment.
Built in Go with proven libraries (go-jira, Gin, Cobra), the server provides a robust MCP interface that handles Jira's REST API complexities. It supports both Jira Cloud and Data Center deployments, with automatic rate limit handling and comprehensive error reporting.
The Docker deployment model means you can run this alongside your existing development stack, or deploy it centrally for team access. Environment-based configuration keeps credentials secure while allowing easy deployment across different environments.
Multiple installation paths accommodate different preferences - pull the Docker image for immediate use, download pre-built binaries for your platform, or build from source if you want to customize functionality.
Ready to eliminate Jira context switching from your workflow? The setup guide walks you through API token creation, server configuration, and AI assistant integration in about 5 minutes.