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Automating Bug Fixes with Sentry MCP, Seer, and GitHub Copilot

Step-by-step guide for integrating Sentry's monitoring and Copilot agents to streamline bug triage and deployment

Engineering ExecutionProduction Support

Sentry's platform now extends beyond crash reporting with MCP (Model Context Protocol) support and its AI assistant called Seer. When combined with GitHub Copilot, teams can build an automated feedback loop that finds defects, ranks them by impact, generates fixes, and submits reviewed pull requests. Below is a practical workflow you can adopt.

1. Capture Context with Sentry MCP

  1. Install Sentry SDK in your application and enable the MCP extension.
  2. Configure MCP to store stack traces, environment data, and release metadata. This persistent context feeds Seer and any Copilot agents that analyze errors later.
  3. Set up role‑based permissions so developers and agents can query historical issues while keeping sensitive data secure.

2. Use Seer for Initial Triage

  1. Enable Seer in your Sentry project. Seer analyzes incoming issues using the MCP context.
  2. It clusters similar errors and assigns an estimated severity score based on user impact.
  3. Seer then suggests owners by inspecting recent commits and code ownership rules.
  4. Push these prioritized issues to GitHub with automatic labels (e.g., priority:high).

3. Generate Fixes with GitHub Copilot

  1. In the repository, create a Copilot workflow that listens for new high‑priority issues.
  2. For each issue, Copilot analyzes the linked stack trace and failing commit using the MCP data stored in Sentry.
  3. It drafts a fix on a new branch and opens a pull request referencing the original Sentry issue.
  4. Attach automated tests generated by Copilot to validate the fix.

4. Review and Merge with Seer's Help

  1. Seer reviews the pull request for regression risks by comparing the change to similar resolved issues in MCP.
  2. It leaves comments or approval suggestions if the risk is low.
  3. Developers perform a final check and merge when satisfied.

5. Deploy and Monitor

  1. Configure your CI pipeline to deploy merged changes automatically.
  2. After deployment, Sentry continues to track any regressions. If a new error appears, the loop begins again with Seer ranking the issue.

Additional Resources

By combining Sentry's observability data with GitHub Copilot's code generation, teams can shorten the time from bug discovery to production deployment. MCP ensures that agents have the full context at every step, while Seer prioritizes issues so engineering effort stays focused on what matters most.