Think inside your AI world.
Jira through Unl
Jira shows the sprint board precisely. Whether the sprint is being run to the rule you set is your judgement — and scope creep looks normal from inside the board.
Jira through Unl reads your live project — issues, sprints, status, docs — against the sprint-commitment rule you have ratified, so drift comes back flagged with the rule it breaks.
What Jira holds
Jira’s MCP surface exposes the delivery surface (with Confluence docs):
- Issues and their status
- Sprints
- Confluence docs and sprint context
What the naked read gives you
A naked read returns the sprint’s issues and burndown. Accurate, and it won’t flag that three issues were added after the sprint started, because “no scope added mid-sprint” is your rule, not a Jira control it enforces.
The frame judges the data it is given; it does not verify the source’s accuracy.
What changes when Jira is read measured
Say you've ratified a sprint-commitment rule: no scope added after day one, and carry-over kept under 20% — rules you set to keep estimates meaningful.
“Is the sprint being run to plan?” returns, measured: no — three issues were added on day four, breaching your no-mid-sprint-scope rule, and projected carry-over is 28%, over your 20% limit. Both are the rules you set to keep the sprint honest. Jira supplied the board; your ratified rule supplied the checks and the reasons.
And back again
If you allow a hotfix as a sanctioned mid-sprint addition and record it, the next read counts it as an allowed exception rather than a breach.
The answer comes back measured against what you already decided, and why.
The lane is live and open to this tool today: one box, paste anything. If it speaks MCP, Unl can reach it. Readings arrive unprompted, the data beside the criterion; Unl is a courier, not a warehouse, and keeps only your keys and the frame.
Questions people ask
Can AI check a Jira sprint against our rules?
Yes, through Unl. You ratify the sprint-commitment rules (no mid-sprint scope, a carry-over limit, with reasons), and the live sprint is measured against them — drift is flagged with the rule it breaks.
How do I connect Jira to Claude?
Jira offers an MCP server over issues, sprints and Confluence docs. Through Unl the same board arrives measured against your ratified rules, so the sprint reads as on-rule or drifting.
Does Unl change my Jira issues?
Unl reads through Jira, and can write back on your explicit gesture — it never acts as a side effect of a read.
What if a ticket was in the sprint on day one but its estimate tripled on day three?
Then no scope was added, by the rule as written, and the read reports the sprint clean on that condition. Your rule counts tickets entering after day one; a ticket that grew was always in it. Whether growth counts as scope is a refinement you may want, and the read will not make it on your behalf.
What this is
Think inside your AI world — you stay in command
Save the thoughts, decisions and targets worth keeping, each with its reasoning, carried into every AI session the moment they matter. A new unit of exchange between you and your AI: the Settled Why with standing that travels. Unprompted.
Your whole AI world. What you decided at the epicentre. It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector — quick to connect, in a couple of steps.
MCP native·Human settled·Model agnostic·Your data
Measured Context
Connect Unl to bring the right information into the moment.
Your sources, read against the criteria you set.
The full product, open. Free at launch.