Managing software projects today requires speed, precision, and alignment across multiple tools and teams. If you’re using Jira to track tasks, epics, and sprints, you’re already halfway there. But what if you could take that further combine the structure of Jira with the intelligence of AI?
Introducing Jira + MCPfy AI, an AI-powered productivity layer that lets you query, automate, and interact with Jira using natural language, all via the Model Context Protocol (MCP).
Let’s break down how you can use this integration to elevate your product and project management workflow.
🚀 Why Combine Jira with MCPfy AI?
Jira is powerful but can be overwhelming, especially when you’re juggling multiple boards, complex JQL queries, and sprint planning. MCPfy.ai turns Jira into an intelligent assistant:
- Ask questions like “What’s pending in Sprint 21?” or “Show me all blockers under Project Phoenix”
- Automate status updates, fetch epics, and review backlogs in real time
- Collaborate across teams without switching tabs or tools
All this happens through MCPfy’s remote server interface with built-in support for LLMs like Claude, OpenAI, Cursor, and Windsurf.
🔧 What You Can Do with Jira MCPfy Tools
MCPfy offers a suite of ready-to-use tools that connect directly to your Jira environment via the MCP protocol. Here’s what’s available:
🔍 Issue Search & Query Tools
jira_search: Search issues using JQL directly, like a pro, just say: “Find all bugs assigned to John this month.”jira_get_issue: Get detailed info about a specific issue, including status, assignee, description, and history.jira_search_fields: Not sure what fields are searchable? Quickly find out using keyword-based field search.jira_get_project_issues: Fetch every issue tied to a specific project, helping you manage end-to-end progress.jira_get_epic_issues: Stay on top of your product roadmap by viewing all issues tied to any epic.
🗖️ Sprint Management Tools
jira_get_sprints_from_board: View sprints from any active board and get a high-level picture of what’s going on.jira_create_sprint: Create new sprints programmatically and optionally define start/end dates, directly from your AI workflow.
🧐 Real Use Cases: How Teams Are Using This Today
1. Daily Standups on Autopilot
Use MCPfy to auto-fetch all blockers, completed tasks, and pending items in a sprint, summarized by your AI assistant before your standup begins.
2. Product Roadmap Updates
Quickly pull epic-level insights across teams. MCPfy can generate summaries like “Here’s what’s done, what’s pending, and what’s at risk in Q3.”
3. Sprint Planning with AI Support
Use jira_get_sprints_from_board + LLM reasoning to analyze team capacity, rollover tasks, and auto-generate next sprint goals.
4. Bug Triage Made Easy
Create an AI-powered bug triage assistant that uses jira_search to identify new issues, classify severity, and assign to relevant engineers.
🛠️ How It Works
You can use MCPfy as a hosted remote MCP server (no-code) or embed its tools directly into your LLM-powered apps. With built-in support for major models and tools, integrating it into your existing stack is seamless.
All you need to get started:
- Your Jira API credentials
- MCPfy server setup
- Choose your favorite LLM (OpenAI, Claude, etc.)
📈 Ready to Boost Your Workflow?
Stop clicking around dashboards and searching through backlogs. Let MCPfy AI do the heavy lifting while you focus on what matters, delivering great products.
Want to see MCP in action? Visit mcpfy.ai and explore how Model Context Protocol can supercharge your AI workflows.
