AI Prompt to Triage Production Errors Using Sentry's MCP Server
This AI prompt is for developers and on-call engineers who connect an AI assistant to Sentry's MCP server and want a fast, consistent first pass on a batch of error reports instead of clicking through each issue manually. Once MCP exposes issue details, stack traces, affected release, and event counts, the prompt tells the model how to group related errors, judge severity, and separate what needs an immediate fix from what can wait.
The prompt deliberately avoids asking the model to "just fix the bug." Stack traces from production often point to a symptom, not the root cause, so the prompt asks for a grounded read of what the trace actually shows, which function or file is implicated, and how confident that read is, rather than a guessed fix that sounds plausible but may be wrong for an issue the model hasn't fully traced through the codebase.
Because Sentry issue payloads can be long and repetitive across similar errors, especially when the same exception fires from multiple call sites, feeding every field into the model without pruning can get expensive fast on large error batches. Running the retrieved payload through Token Optimizer before triage, to strip redundant stack frames and duplicate context, keeps the prompt focused on what's actually needed to triage severity.
Prompt template
Want it sharper? Optimize this prompt with Prompt Optimizer, check it with the Prompt Debugger or shorten it with the Token Optimizer.
Example input
When to use it
- You're starting a shift and need a quick severity-ranked view of overnight Sentry issues
- A new release just shipped and you want new or spiking errors flagged before they pile up
- You're deciding which of several open issues to assign first and need a reasoned priority order
- You want a plain-language summary of a stack trace to share with a non-engineering stakeholder
Best practices
- Give the model the actual release version and rollout time so it can tell newly introduced errors from pre-existing ones
- Ask it to state its confidence in identifying the root cause separately from the severity ranking
- List any known, already-tracked low-priority issues so the model excludes them instead of re-flagging them
- Have it group duplicate errors from different call sites into one item instead of listing each occurrence separately
Common mistakes
- Asking the model to propose a code fix directly from a stack trace without having it read the actual implicated file first
- Not providing release or deploy timing, so the model can't tell a new regression from a long-standing low-priority error
- Treating every error with a high event count as automatically high severity, even when it's a known, harmless edge case
- Skipping a check of the top 2-3 flagged issues against the raw Sentry data before escalating them
FAQs
Can an AI assistant actually read my Sentry issues without me copying stack traces manually?
Only if it's connected through Sentry's MCP server or a similar integration. Without that connection, you'd still need to paste in the issue data yourself.
Should I let the model's severity ranking override my own on-call judgment?
No. Treat it as a fast first pass that uses the metrics you gave it, not a replacement for engineering judgment, especially for issues the model flagged as low-confidence on root cause.
Why does this prompt ask the model not to suggest a code fix?
A stack trace alone often shows a symptom, not the actual root cause in your codebase. Having the model guess a fix without reading the implicated file risks a plausible-sounding but wrong suggestion.
How can I check this prompt's quality before running it against a live Sentry project?
Intelligence Score — run your filled-in version through it to catch vague constraints or missing context before you point it at real production data.