Automated Error Analysis with Sentry MCP

What You'll Build

An automated error monitoring system that uses Continue CLI with Sentry MCP to analyze production errors, identify root causes with AI, and create detailed GitHub issues with suggested fixes.

What You’ll Learn

This cookbook teaches you to:

Use Sentry MCP to access issues
Analyze error patterns and stack traces with AI
Automatically create GitHub issues with root cause analysis
Set up continuous error monitoring with GitHub Actions

Prerequisites

Before starting, ensure you have:

GitHub repository where you want to create issues
Sentry account with an active project collecting errors
Node.js 18+ installed locally
Continue CLI with active credits (required for API usage)
GitHub CLI installed (gh command)

Install Continue CLI

npm i -g @continuedev/cli

Set up Continue CLI Account & API Key

Visit Continue Organizations
Sign up or log in to your Continue account
Navigate to your organization settings
Click “API Keys” and then ”+ New API Key”
Copy the API key immediately (you won’t see it again!)
Login to the CLI: cn login

Continue CLI handles complex error analysis and API interactions - you just need to provide the right prompts!

Step 1: Set Up Your Credentials

First, you’ll need to gather your Sentry and GitHub API credentials.

Configure Sentry MCP
Sentry API Credentials
Set Up GitHub CLI Authentication

See Sentry MCP Documentation for detailed configuration options

The Sentry MCP supports multiple configuration methods. For Continue CLI, OAuth is recommended:Option 1: OAuth Configuration (Recommended)The Sentry MCP will prompt for OAuth authentication when first used. Simply follow the authorization flow.Option 2: STDIO Mode with Auth TokenFor local development or self-hosted Sentry installations, you can use STDIO mode:

npx @sentry/mcp-server@latest --access-token=YOUR_SENTRY_TOKEN --host=sentry.io

Or use environment variables:

SENTRY_ACCESS_TOKEN=your-token SENTRY_HOST=sentry.io

The --host parameter is required and should point to your Sentry instance (e.g., sentry.io or sentry.example.com for self-hosted).

Sentry Error Monitoring Workflow Options

Fastest Path to Success

Skip the manual setup and use our pre-built Sentry Continuous AI agent that includes optimized prompts, rules, and the Sentry MCP for more consistent results.

How Sentry MCP Works:

Connects to your Sentry organization via OAuth
Provides tools for accessing issues, projects, teams, and DSNs
Supports both hosted (https://mcp.sentry.dev) and self-hosted Sentry instances
Automatically handles authentication and API interactions

⚡ Quick Start (Recommended)
🛠️ Manual Setup

Perfect for: Immediate error analysis with AI-powered root cause detection and built-in debugging

Add the Pre-Built Agent

Visit the Sentry Continuous AI Agent on Continue Hub and click “Install Agent” or run:

cn --config continuedev/sentry-continuous-ai

This agent includes:

Optimized prompts for Sentry error analysis and GitHub issue creation
Built-in rules for consistent formatting and error handling
Sentry MCP for more reliable API interactions
Automatic authentication via OAuth flow

Run Error Analysis

Navigate to your project directory and enter this prompt in the Continue CLI TUI:

Analyze recent Sentry errors and create GitHub issues for critical bugs with suggested fixes

That’s it! The agent handles everything automatically.

Why Use the Agent? Results are more consistent and debugging is easier thanks to the Sentry MCP integration and pre-tested prompts.

Why GitHub CLI over GitHub MCP: While GitHub MCP is available, it can be token-expensive to run. The gh CLI is more efficient, requires no API tokens (authenticated via gh auth login), and provides a cleaner command-line experience. GitHub MCP remains an option if you prefer full MCP integration.

Agent Requirements

To use the pre-built agent, you need either:

Continue CLI Pro Plan with the models add-on, OR
Your own API keys added to Continue Hub secrets (same as Step 1)

The agent will automatically detect and use your configuration. For Sentry MCP:

Sentry account with at least one project
User Auth Token with appropriate scopes (or OAuth flow)
The MCP works with both Sentry’s hosted service (sentry.io) and self-hosted instances

Repository Labels Required: Make sure your GitHub repository has these labels:

bug, sentry, production
critical, high-priority, medium-priority, low-priority
needs-investigation, has-fix

Create missing labels in your repo at: Settings → Labels → New label

Step 2: Analyze Sentry Errors with AI

Use Continue CLI to perform intelligent error analysis. Enter these prompts in the Continue CLI TUI:

Recent Errors Analysis
Critical Error Investigation
Performance Issue Detection

Prompt:

Show me Sentry errors from the past 7 days, grouped by error type, with frequency counts

Available Sentry MCP Tools:

Organizations: Access org-level data and settings
Projects: Query projects and their configurations
Issues: Search and analyze error issues
Teams: Manage team assignments
DSNs: Retrieve project DSN configurations

Step 3: Automate GitHub Issue Creation

Create actionable GitHub issues from Sentry errors. Enter this prompt in the Continue CLI TUI: Prompt:

For each unresolved Sentry error with 'critical' or 'high' severity:
1. Analyze the error using Sentry MCP
2. Create a GitHub issue with gh CLI:
   - Title: '🐛 [Sentry] [Error Type]: Brief description'
   - Body with:
     * Error summary and impact (affected users, frequency)
     * Full stack trace
     * Environment details (browser, OS, release version)
     * Link to Sentry issue
     * Root cause analysis from AI
     * Suggested fix with code snippets
     * Related Sentry issues
   - Labels: 'bug', 'sentry', 'production', and severity label
   - Assignees: Team member based on code ownership
3. Update Sentry issue with GitHub issue link
4. Confirm creation with GitHub issue URL

Best Practice: Link GitHub issues back to Sentry for full traceability. This creates a bidirectional connection between your error monitoring and issue tracking.

Step 4: Set Up Continuous Monitoring with GitHub Actions

Automate error monitoring with the Sentry Release GitHub Action and Continue CLI to create comprehensive, AI-powered issue descriptions:

Why Combine Sentry Releases with Continue CLI?

Release Tracking: Associate errors with specific deployments
AI-Powered Analysis: Continue CLI generates detailed issue descriptions with root cause analysis
Better Context: Link errors to commits and pull requests
Automated Workflows: Create issues with full stack traces and suggested fixes

name: Sentry Error Monitoring

on:
  push:
    branches:
      - main
  schedule:
    # Run every 6 hours
    - cron: "0 */6 * * *"
  workflow_dispatch: # Allow manual triggers

jobs:
  monitor-errors:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - uses: actions/setup-node@v4
        with:
          node-version: "22"

      # Create Sentry release for better error tracking
      - name: Create Sentry release
        uses: getsentry/action-release@v1
        env:
          SENTRY_AUTH_TOKEN: ${{ secrets.SENTRY_AUTH_TOKEN }}
          SENTRY_ORG: ${{ secrets.SENTRY_ORG }}
          SENTRY_PROJECT: ${{ secrets.SENTRY_PROJECT }}
        with:
          environment: production

      - name: Install Continue CLI
        run: |
          npm install -g @continuedev/cli
          echo "✅ Continue CLI installed"

      - name: Authenticate GitHub CLI
        run: |
          echo "${{ secrets.GITHUB_TOKEN }}" | gh auth login --with-token
          echo "✅ GitHub CLI authenticated"

      - name: Analyze Sentry Errors and Create Issues
        env:
          CONTINUE_API_KEY: ${{ secrets.CONTINUE_API_KEY }}
          SENTRY_AUTH_TOKEN: ${{ secrets.SENTRY_AUTH_TOKEN }}
          SENTRY_ORG: ${{ secrets.SENTRY_ORG }}
          SENTRY_PROJECT: ${{ secrets.SENTRY_PROJECT }}
        run: |
          echo "🔍 Analyzing Sentry errors..."
          
          # Use Continue CLI to analyze errors and generate comprehensive issue descriptions
          cn -p "Using Sentry MCP, analyze errors from the past 6 hours for project $SENTRY_PROJECT:
                 1. Filter for unresolved errors with high or critical severity
                 2. Group similar errors to avoid duplicates
                 3. For each unique critical error:
                    - Generate a comprehensive issue description including:
                      * Error summary with frequency and user impact metrics
                      * Full stack trace with highlighted problem areas
                      * Environment details (browser, OS, release version)
                      * Link to Sentry issue dashboard
                      * Root cause analysis using AI
                      * Step-by-step reproduction if available
                      * Suggested fix with code examples
                      * Related errors or patterns
                    - Check if a GitHub issue already exists for this error
                    - If not, create a new issue with the generated description
                    - Use gh CLI: gh issue create --title '[Sentry] [Error Type]: Brief description' --body 'AI-generated comprehensive analysis' --label 'bug,sentry,critical,needs-investigation'
                    - Link the GitHub issue back to Sentry
                 4. Generate a summary report with:
                    - Total errors analyzed
                    - New issues created with URLs
                    - Errors skipped (already tracked)
                    - Release correlation if available

                 Sentry Organization: $SENTRY_ORG
                 Project: $SENTRY_PROJECT
                 Only process errors not already tracked in GitHub."

      - name: Post workflow summary
        if: always()
        run: |
          echo "## 📊 Sentry Error Monitoring Summary" >> $GITHUB_STEP_SUMMARY
          echo "✅ Workflow completed at $(date)" >> $GITHUB_STEP_SUMMARY
          echo "" >> $GITHUB_STEP_SUMMARY
          echo "Check the 'Analyze Sentry Errors' step above for:" >> $GITHUB_STEP_SUMMARY
          echo "- Number of errors analyzed" >> $GITHUB_STEP_SUMMARY
          echo "- GitHub issues created" >> $GITHUB_STEP_SUMMARY
          echo "- Errors already tracked" >> $GITHUB_STEP_SUMMARY

Required GitHub Secrets:

CONTINUE_API_KEY: Your Continue API key from hub.continue.dev/settings/api-keys
SENTRY_AUTH_TOKEN: Your Sentry User Auth Token (needs scopes: org:read, project:read, project:releases, event:read)
SENTRY_ORG: Your Sentry organization slug
SENTRY_PROJECT: Your Sentry project slug
GITHUB_TOKEN: Automatically provided by GitHub Actions

Add these at: Repository Settings → Secrets and variables → Actions

Workflow Best Practices:

Run every 6 hours to catch critical errors quickly
Create Sentry releases on push to track error-to-deployment correlation
Use Continue CLI to generate comprehensive, AI-powered issue descriptions
Use duplicate detection to avoid creating multiple issues for the same error
Filter by severity to focus on high-impact issues
Include full error context and suggested fixes in issues
Tag issues with appropriate labels for team routing
Link GitHub issues back to Sentry for bidirectional tracking

What You’ve Built

After completing this guide, you have a complete Sentry-powered error monitoring system that:

Monitors production errors - Automatically fetches and analyzes Sentry issues every 6 hours
Identifies critical bugs - Uses AI to spot high-impact errors
Creates actionable tasks - Generates GitHub issues with root cause analysis and suggested fixes
Runs autonomously - Operates continuously without manual intervention using GitHub Actions
Scales with your app - Handles growing error volumes and complexity automatically

Continuous AI Error Monitoring

Your system now operates at Level 2 Continuous AI - AI handles routine error analysis with human oversight through GitHub issue review and resolution.

Advanced Error Analysis Prompts

Enhance your workflow with these advanced Continue CLI prompts:

Release Impact Analysis

Compare error rates before and after the latest Sentry release to identify regressions introduced in the deployment

Error Trend Detection

Analyze Sentry error trends over the past 30 days and identify emerging issues before they become critical

User Impact Assessment

Identify which errors are affecting the most unique users and prioritize fixes based on user impact

Performance Correlation

Cross-reference Sentry performance issues with error spikes to identify root causes

Security Best Practices

Protect Your API Keys:

Store all credentials as GitHub Secrets, never in code
Use Continue CLI’s secure secret storage
Limit Sentry token scopes to minimum required permissions
Rotate API keys regularly (every 90 days recommended)
Monitor token usage for unusual activity
Use OAuth when possible for better security

Troubleshooting

Sentry MCP Connection Issues

If you encounter connection issues:

Verify OAuth authentication is complete
Check your Sentry organization access
Ensure the MCP server URL is correct (https://mcp.sentry.dev/mcp)
For self-hosted Sentry, verify your host URL is configured correctly

See the Sentry MCP GitHub Issues for known issues and solutions.

Common Error Analysis Issues

Issue	Solution
No errors returned	Verify your Sentry project has collected errors recently
OAuth prompt not appearing	Check that Continue CLI has proper MCP configuration
Duplicate GitHub issues	Implement duplicate detection in your prompts
Missing error context	Ensure your Sentry token has `event:read` scope

Next Steps

Set up Sentry performance monitoring
Configure Sentry release tracking for deployment correlation
Integrate Slack MCP for error alerts
Join the Continue Discord for support

Guides

Cookbooks

Automated Error Analysis with Sentry MCP

What You'll Build

What You’ll Learn

Prerequisites

Step 1: Set Up Your Credentials

Sentry Error Monitoring Workflow Options

Fastest Path to Success

Step 2: Analyze Sentry Errors with AI

Step 3: Automate GitHub Issue Creation

Step 4: Set Up Continuous Monitoring with GitHub Actions

What You’ve Built

Continuous AI Error Monitoring

Advanced Error Analysis Prompts

Release Impact Analysis

Error Trend Detection

User Impact Assessment

Performance Correlation

Security Best Practices

Troubleshooting

Sentry MCP Connection Issues

Common Error Analysis Issues

Next Steps

Resources

Guides

Cookbooks

What You'll Build

​What You’ll Learn

​Prerequisites

​Step 1: Set Up Your Credentials

​Sentry Error Monitoring Workflow Options

Fastest Path to Success

​Step 2: Analyze Sentry Errors with AI

​Step 3: Automate GitHub Issue Creation

​Step 4: Set Up Continuous Monitoring with GitHub Actions

​What You’ve Built

Continuous AI Error Monitoring

​Advanced Error Analysis Prompts

Release Impact Analysis

Error Trend Detection

User Impact Assessment

Performance Correlation

​Security Best Practices

​Troubleshooting

​Sentry MCP Connection Issues

​Common Error Analysis Issues

​Next Steps

​Resources

What You’ll Learn

Prerequisites

Step 1: Set Up Your Credentials

Sentry Error Monitoring Workflow Options

Step 2: Analyze Sentry Errors with AI

Step 3: Automate GitHub Issue Creation

Step 4: Set Up Continuous Monitoring with GitHub Actions

What You’ve Built

Advanced Error Analysis Prompts

Security Best Practices

Troubleshooting

Sentry MCP Connection Issues

Common Error Analysis Issues

Next Steps

Resources