Production issues aren't painful because fixing bugs is hard. They're painful because finding where the bug originated can take hours.


This is where MCP (Model Context Protocol) comes in. MCP is an open standard that allows AI assistants to connect with external tools and data sources, such as databases, monitoring platforms, application logs, and project management tools. It enables AI to interact with these systems and gather the context needed to investigate complex issues.


Hi I'm Shakti, a Software Engineer at a bug healthcare company. Recently I've been working closely with production issues and I've realized one thing time is everything when dealing with production incidents.

The real challenge isn't just fixing the bug. It's identifying where it originated, performing Root Cause Analysis (RCA), and reproducing the issue in lower environments.

All these steps can be extremely time-consuming.

But don't worry, I've got you!

Here are some practical tips on how you can use AI to triage production issues faster, identify root causes, and save valuable debugging time.

Step 1: Identify and Collect All the MCP Tools We Need

Start by identifying all the tools you manually use while triaging production issues.

For example:

  • Database MCP: Investigate database records and validate data. Make sure you have read-only access.
  • Grafana / New Relic MCP: Analyze l, dashboards, and application performance.
  • Browser Developer Tools MCP: Inspect network requests, API responses, and browser console errors.
  • CloudWatch / Azure Logs MCP: Investigate application and infrastructure logs.
  • Jira / Rovo MCP: Fetch ticket details, comments, requirements, and related issues.

Basically think about every tool you manually open when investigating a production issue and check whether an MCP is available for it.

Step 2: Verify Permissions and Access

Having an MCP integration doesn't automatically mean you have access to everything.

Check whether your account has the required permissions to access these tools, especially production databases, monitoring dashboards, application logs, and Jira projects.

Make sure you follow your organization's security and access policies. Prefer read-only access wherever possible.

Step 3: Connect Everything to Your AI Harness

Once you've identified the required MCP servers and verified your permissions, connect them to your primary AI harness or the AI assistant you use daily.

For example, this could be Claude, Cursor, or another AI environment that supports MCP integrations.

Step 4: Verify Your Integrations

Now that you've connected all the tools, verify that your AI assistant can actually communicate with them.

Test a few basic operations:

  • Can it fetch Jira tickets?
  • Can it query the database?
  • Can it retrieve application logs?
  • Can it access Grafana or New Relic metrics?

Don't move forward until you've verified that the integrations are working as expected.

Step 5: Create a Dedicated Project for Production Issue Triage

This is where things get interesting!

Create a separate project in your AI harness specifically for production issue triaging.

Give this project a detailed system prompt that defines the AI's role as an experienced Production Support Engineer who understands debugging, root cause analysis, log investigation, and issue reproduction.

Your instructions should tell the AI to:

  1. Understand the production issue from the Jira ticket.
  2. Identify the affected services, APIs, and components.
  3. Investigate relevant application logs and monitoring metrics.
  4. Check database records wherever relevant and permitted.
  5. Identify potential root causes using the available evidence.
  6. Suggest ways to reproduce the issue in lower environments.
  7. Correlate findings across different tools.
  8. Generate a concise investigation report containing findings, supporting evidence, probable root cause, and recommended next steps.

Important: Instruct the AI not to assume a root cause without evidence. It should clearly distinguish confirmed findings from hypotheses.

Step 6: Execute and Let AI Investigate

Now comes the fun part!

Simply take the Jira ticket associated with the production issue and pass it to your dedicated AI project.

Your AI assistant can start investigating the issue using all the connected MCP tools, correlate information across different systems, and generate an investigation report.

Instead of manually switching between Jira, Grafana, databases, and log management platforms, you now have one place to coordinate the investigation.

The goal isn't to replace your debugging skills with AI. It's to eliminate repetitive investigation work so you can focus on understanding and resolving the actual problem.

Final Thoughts

AI won't magically fix every production issue, but with the right MCP integrations, context, and instructions, it can significantly reduce the time spent on investigation and debugging.

Stop spending hours collecting logs and switching between tools. Start using AI to connect the dots.