Most teams triage errors the same way. Check the error tracker in the morning, skim the stack traces, pick the ones that look urgent, start investigating. The rest pile up. By the time anyone gets to the long tail of production errors, the context is stale and the motivation is gone.
What if that first pass happened automatically?
We’ve been experimenting with a workflow that connects Scout’s error data to AI assistants through our MCP server. The idea is simple: let the machine do the investigation overnight so your team can start the day reviewing proposed solutions instead of digging through stack traces.
How It Works
A scheduled job runs on whatever cadence makes sense for your team. It connects to Scout’s MCP server, pulls recent error groups, and hands each one to an AI assistant along with access to your codebase. The assistant reads the error context, traces the root cause through your code, proposes a fix, and opens a draft pull request with its analysis.
By the time your team opens their laptops, there’s a queue of draft PRs waiting. Each one includes what went wrong, why, and a proposed change. No one has to merge anything. No one has to trust the machine. It’s just a head start.
What Scout’s MCP Server Brings to This
The piece that makes this work well (rather than just possible) is context. An error message and a stack trace alone don’t tell you much. Scout’s MCP server gives the AI assistant access to the full picture:
- Error groups via
GetAppErrorGroupsTool. Exception class, message, location, occurrence count, and how recently it started showing up. - Full request traces via
GetAppTraceTool. Every span in the request where the error occurred. What the app was doing, how long each piece took, where time was spent. - Performance insights via
GetAppInsightsTool. N+1 queries, memory bloat, slow queries. Sometimes the error isn’t the real problem. It’s a symptom of something deeper.
This is the same data your team would review manually. The AI assistant just gets through it faster.
Putting It Together
The architecture is intentionally boring:
A scheduler. GitHub Actions, a cron job, whatever your team already uses. This kicks off the process.
An error collection step. Authenticate with Scout’s MCP server via OAuth, pull error groups from your chosen timeframe, and filter out the noise (transient failures, third-party issues, known configuration problems).
An analysis step. For each error worth investigating, give the AI assistant the error data from Scout and access to the relevant repository. Let it trace through the code, identify the root cause, and write a minimal fix with tests.
A PR step. Open a draft PR for each proposed fix. Include the root cause analysis in the description so reviewers have context without having to re-investigate.
On Security
A few things that matter here.
Credentials. Store your Scout OAuth token and GitHub tokens as secrets. Never hardcode them. Scout’s MCP server is read-only, but treat the token like any production credential.
Scope your access. The AI assistant needs write access to create branches and PRs. Use a dedicated GitHub App or fine-grained token scoped to only the repos you want. Never give an automated workflow org-wide permissions.
Sanitize error data. Production errors sometimes carry sensitive information in request parameters or headers. Strip anything that isn’t the exception class, message, stack trace, and timing data before passing it to the AI assistant.
Draft PRs only. Automated fixes never merge without human review. This is non-negotiable. Your existing branch protection rules should enforce this, but verify before you ship the workflow.
Start Smaller Than You Think
The temptation is to point this at every error in production and see what happens. Don’t. Start with one error class you understand well. Validate that the AI-generated fixes make sense. Check that the root cause analysis is accurate. Then expand.
A good rule of thumb: if a human developer wouldn’t investigate the error by reading your codebase, the AI assistant shouldn’t either. Configuration issues, network timeouts, and third-party outages don’t need code fixes.
Five thoughtful draft PRs are worth more than fifty noisy ones. Set a quality threshold and discard anything below it.
Getting Started
If you’re using Scout for error monitoring, you already have everything you need. The MCP server is available on all plans. Connect, authenticate, and start pulling error data. Setup docs are at scoutapm.com/mcp.
If you’re currently using a different error monitoring tool and want to try this approach, the MCP server gives you errors, traces, and performance context through a single interface.
Questions about building this? Reach out at support@scoutapm.com. We’ve been running variations of this workflow internally and are happy to walk through the details.
For application monitoring with errors, logs, and traces, Scout Monitoring provides the fastest insights without the bloat.