atlassian-jira · MCP Server

Jira + Improvado MCP — Sprint Data Without JQL

Improvado connects Jira to Claude, ChatGPT, and any MCP-compatible AI agent. Query sprint progress, cycle time, backlog debt, and team velocity in plain English — no JQL, no dashboard clicking.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Get Any Jira Metric Without Writing JQL

Stop building JQL filters and exporting CSV reports. Ask your AI agent for velocity trends, overdue issues, cycle time by team, or blocker counts — across any project, sprint, or assignee. The MCP server handles Jira API queries.

Example prompts

"Which high-priority issues have been in 'In Progress' status for more than 5 days this sprint?"

15 min → 20 sec

"Show me average cycle time per engineer for the last 3 sprints, broken down by issue type."

30 min → 30 sec

"Compare velocity across all active teams for Q1 vs Q2. Which teams are trending down?"

2 hrs → 2 min

Works with Claude ChatGPT Cursor +5

Write

Write: Update Issues and Sprints Through Conversation

Your AI agent doesn't just read Jira data — it acts on it. Update issue statuses, reassign tasks, move backlog items, and bulk-edit labels through natural language instead of clicking through the Jira UI.

Example prompts

"Move all unestimated issues in the current sprint to the backlog. Flag them as 'needs-estimation'."

20 min → 30 sec

"Reassign all open issues from a departing team member to the team lead as interim owner."

30 min → 1 min

"Create sub-tasks for the API gateway epic: auth layer, rate limiting, and error handling. Assign to backend team."

25 min → 5 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Track Sprint Health Without Dashboard Babysitting

Set up watches on sprint metrics that matter. Your AI monitors Jira continuously and flags scope creep, velocity drops, and stale blockers before they derail the release.

Example prompts

"Alert me if any sprint accumulates more than 10% scope creep after day 3."

Manual → auto

"Every Monday at 9am: send a sprint health digest — velocity, blocked issues, and carry-over risk."

1.5 hrs → auto

"Flag any issue marked as a blocker that has had no activity for more than 48 hours."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Sprint Reviews Take Hours to Prepare

The problem

Sprint reviews require pulling velocity data, calculating completion rates, listing carry-over stories, and summarizing blockers — all from Jira. Doing this manually across five teams means 90 minutes of JQL queries, CSV exports, and spreadsheet assembly every two weeks.

How MCP solves it

Improvado extracts full sprint-level data from Jira and makes it queryable via AI. Ask for a sprint summary in one prompt — completion rates, velocity trends, carry-over stories, and blocker history. Ready in seconds, not 90 minutes.

Try asking

Summarize the last sprint for the platform team: stories completed, carry-over, blockers, and velocity vs the prior sprint.

Answer in seconds

All data sources, one query

Challenge 2

Technical Debt Is Invisible Until It Breaks Something

The problem

Backlog hygiene reports don't exist in Jira by default. Teams don't know how many issues are unestimated, how many have been sitting in backlog for over 90 days, or which epics have stalled. Debt accumulates silently.

How MCP solves it

Improvado normalizes Jira issue data into a structured model with age, status history, and epic context. The MCP server lets your AI audit the backlog instantly — unestimated items, stale epics, and prioritization gaps in one query.

Try asking

How many backlog issues are unestimated and older than 60 days? Group by epic and team.

Full detail preserved

No data loss on export

Challenge 3

Cross-Team Dependency Tracking Is Manual

The problem

Linked issues and cross-project dependencies in Jira are visible one ticket at a time. When a shared infrastructure epic blocks three product teams, nobody has a consolidated view of the impact. Dependency mapping happens in weekly meetings, not in Jira.

How MCP solves it

Improvado models Jira issue links and project relationships. The MCP server lets your AI map dependencies across projects — which teams are blocked, by which issues, and how long those blockers have been open.

Try asking

Which open issues in the infrastructure project are blocking tasks in other teams' sprints right now?

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What Jira data can I access through the MCP server?

Issues (status, assignee, priority, labels, custom fields), sprint data (velocity, completion rates, carry-over), epics and roadmap progress, cycle time and lead time, backlog age, issue links and dependencies, and project-level health metrics.

Do I need to know JQL to use this?

No. That's the point. You ask in plain English — 'show me all blocked issues in the current sprint' — and the MCP server translates that into the appropriate Jira API call. JQL knowledge is optional, not required — all through Improvado's hosted MCP server.

Does this work with Jira Cloud and Jira Data Center?

Yes. Improvado supports both Jira Cloud (via REST API) and Jira Data Center (self-hosted). You provide your Jira instance URL and API credentials during setup. Both environments support read and write operations.

Which AI tools work with this Jira MCP server?

Any MCP-compatible client — Claude Desktop, ChatGPT, Cursor, Windsurf, Gemini, and custom applications using MCP HTTP transport. Claude is the most commonly used due to native MCP support — all through Improvado's hosted MCP server.

Can the AI write back to Jira or just read data?

Both. Read operations cover all issue, sprint, and project data. Write operations include creating and updating issues, bulk status changes, reassignments, adding comments, and moving issues between sprints. Permissions are scoped to your Jira API token.

How long does setup take?

If you're already an Improvado user, connect Jira in the integrations panel and start querying at app.improvado.io/agent. For Claude Desktop or Cursor, add one line to your MCP config — under 60 seconds.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms