Think inside your AI world.
Sentry through Unl
“Is it safe to ship?” looks like an error question. It’s a frame question — safe by whose bar? — and the bar lives with you, not in Sentry.
Sentry through Unl reads your live error picture — projects, issue detail, error volume — against the ship-safety bar you have ratified, so “is it safe to ship?” returns safe-or-not with the breaching issue named, not a list of errors to weigh yourself.
What Sentry holds
Sentry’s MCP server exposes the observability surface (across 150k+ organisations):
- Projects and their health
- Issue details and error retrieval
- Seer fix flows
What the naked read gives you
A naked read lists the open issues and their event counts. Accurate, and it leaves “safe to ship?” entirely to your judgement, because your safety bar isn’t a Sentry field.
The frame judges the data it is given; it does not verify the source’s accuracy.
What changes when Sentry is read measured
Say your ship-safety bar is two conditions: no new unresolved error above 50 events in the last 24 hours, and nothing open in the payment path.
“Safe to ship?” returns, measured: not safe — a new error in the checkout path crossed 60 events overnight, which breaches the payment-path condition you set precisely because that path can’t regress. Sentry supplied the errors; your ratified bar turned them into a go/no-go with the reason.
And back again
When you triage that error as a known third-party blip and record it, the next safety read discounts it — the exception is held, not re-litigated.
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.
Read further
Questions people ask
Can AI tell me if it's safe to ship from Sentry?
Reading Sentry alone gives the errors, not the verdict. Through Unl you ratify a ship-safety bar (say no new high-volume errors, nothing open in a critical path), and the live error picture is measured against it — safe or not, with the breaching issue named.
How do I connect Sentry to Claude?
Sentry runs an MCP server for direct access to projects and issues. Through Unl the same error data arrives measured against your ratified safety bar, so “is it safe?” becomes an answerable question.
Does Unl change anything in Sentry?
Unl reads through Sentry, and can write back on your explicit gesture — it never acts as a side effect of a read.
What if the payment path is not tagged as such in Sentry?
Then the second half of your bar cannot be tested, and the first half passing does not complete it. Sentry can count events and tell you what is unresolved; which issues belong to the payment path is a boundary you hold, not a field it keeps. The read reports that condition as unevaluated rather than calling the release safe on the half it could measure.
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.