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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.
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.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
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.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
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.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
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.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
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.
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.
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.
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.
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.
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.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
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.
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.
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.
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.
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.
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.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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