Jira MCP
Manage Atlassian Jira issues from your AI agent
How to Install Jira MCP
$npx mcp-jiraRequires Claude Desktop, Cursor, Windsurf, or another MCP-compatible client.
About Jira MCP
The Jira MCP server lets AI agents create, update, search, and transition Jira issues. Agents can read sprint boards, query backlogs, assign tickets, and post comments — ideal for autonomous engineering workflows.
Jira MCP is a Developer Tools MCP server designed for professional engineers and engineering teams. It enables wiring AI assistants into the inner loop of writing and shipping code by exposing Git, package, build, and CLI operations as MCP tools. Key capabilities include git and github operations (status, blame, prs), package and dependency inspection, build and test runner integration, and structured tool calls that work cleanly inside an LLM context. It integrates with Git, GitHub, GitLab, npm, pnpm, Docker, and common build systems, and is best suited for professional engineers and engineering teams who need reviewing diffs and opening prs from chat.
Key Features
- jira
- atlassian
- project-management
- ticketing
- Git and GitHub operations (status, blame, PRs)
- Package and dependency inspection
Pricing
- Core MCP server
- Community support
- Works with any MCP client
- Everything in Free
- Higher usage limits
- Priority support
- Everything in Pro
- SLA & SSO
- Dedicated support
Tier details are indicative — visit the Jira MCP website for current pricing.
Pros & Cons
Pros
- Acts more like a pair programmer with real tools
- Tight scoping (one repo, dry-run) limits blast radius
- Keeps flow inside the editor
Cons
- Write operations require explicit guardrails
- Quality of output depends on repo hygiene
- Larger monorepos may need extra filtering
Best For
- Reviewing diffs and opening PRs from chat
- Triaging CI failures
- Scaffolding new packages or modules
- Repo-wide refactors with verification
Screenshots
Screenshots coming soon —
Submit yours →Our Take on Jira MCP
Most capable Jira MCP — must-have for teams running engineering workflows through AI agents.