---

name: sentry-issue-fixer description: Fetch, analyze, fix Sentry issues, run tests, and create PRs invocation: user

Sentry Issue Fixer

Automatically fetch the most important open Sentry issue, analyze it, implement a fix, verify with tests, and create a pull request.

Prerequisites

• Sentry MCP server connected (configured in ~/.claude/settings.json)
• GitHub CLI (gh) authenticated
• Docker running (for integration tests)
• Python 3.12 with virtualenv
• Checkout main branch and pull the latest changes

MCP Server Configuration

The Sentry MCP server should be configured in ~/.claude/settings.json:

{
  "mcpServers": {
    "Sentry": {
      "url": "https://mcp.sentry.dev/mcp"
    }
  }
}

On first use, you'll be prompted to authenticate with Sentry via OAuth.

Workflow

Step 1: Fetch Most Important Open Issue

Use Sentry MCP tools to get the highest priority unresolved issue:

1. First, list available organizations:
   mcp__Sentry__list_organizations

2. List projects in the organization:
   mcp__Sentry__list_projects with organization_slug

3. Search for unresolved issues sorted by priority/frequency:
   mcp__Sentry__search_issues with:
   - organization_slug
   - project_slug
   - query: "is:unresolved"
   - sort: "freq" or "priority"

4. Get detailed issue information:
   mcp__Sentry__get_issue with issue_id

Step 2: Analyze the Issue

Use Sentry's analysis tools:

1. Get issue details and stack trace:
   mcp__Sentry__get_issue_details

2. Find errors in specific files:
   mcp__Sentry__find_errors_in_file with filename from stack trace

3. Use Seer AI for root cause analysis (if available):
   mcp__Sentry__invoke_seer_agent for AI-powered fix suggestions

Gather from analysis:

• Exception type and message
• Stack trace (file, function, line number)
• Error frequency and affected users
• Environment context (tags, release, etc.)
• Pattern of occurrences

Step 3: Plan the Fix

Before implementing, create a plan:

1. Root Cause Analysis

   • What is causing the error?
   • Is it a logic error, missing validation, race condition, etc.?

2. Proposed Solution

   • What changes are needed?
   • Which files need modification?
   • Are there related issues to consider?

3. Risk Assessment

   • Could this fix break other functionality?
   • Does it need backward compatibility?

4. Testing Strategy

   • What unit tests should be added/modified?
   • What integration tests are relevant?

Step 4: Implement the Fix

1. Create a Feature Branch

   git checkout main
   git pull origin main
   git checkout -b fix/sentry-<issue-id>-<short-description>
   

2. Make Code Changes

   • Implement the planned fix
   • Add appropriate error handling
   • Add or update tests

3. Commit Changes

   git add <files>
   git commit -m "fix: <description>
   
   Fixes Sentry issue <issue-id>
   
   Co-Authored-By: Claude <noreply@anthropic.com>"
   

Step 5: Run ALL Test Suites

IMPORTANT: You MUST run ALL of the following test suites before creating a PR. Do NOT skip any.

5.1 DB/SQLAlchemy Tests (Primary Backend Tests - Dockerized)

docker compose -f tests/db-sqlalchemy/docker-compose.yml --project-directory . run --build --rm tests

5.2 Backend Unit Tests

.venv/bin/pytest tests/unit/ -v --tb=short --ignore=tests/unit/test_rpc_endpoint_manager.py

5.3 Frontend Unit Tests

cd frontend && npm run test

If any tests fail:

• Analyze the failure
• Fix the issue
• Re-run ALL test suites until they pass

Step 6: Run Integration Tests (Optional - if Docker services are running)

Follow the integration-tests skill:

# Ensure Docker is running with the studio
docker compose ps  # Verify services are up

source .venv/bin/activate
export PYTHONPATH="$(pwd)"

# Run integration tests in parallel (excluding test_validators.py)
gltest --contracts-dir . tests/integration -n 4 --ignore=tests/integration/test_validators.py

# Run validator tests separately
gltest --contracts-dir . tests/integration/test_validators.py

If tests fail:

• Check Docker logs: docker compose logs -f
• Analyze test output
• Fix issues and re-run

Step 7: Create Pull Request

Follow the create-pr skill:

# Push branch
git push -u origin $(git branch --show-current)

# Create PR with Sentry context
gh pr create --title "fix: <description from Sentry issue>" --body "$(cat <<'EOF'
Fixes Sentry issue: <link-to-sentry-issue>

# What

- [Describe the error that was occurring]
- [List the changes made to fix it]

# Why

- This error was affecting [X users / occurring Y times]
- Root cause: [explanation]

# Testing done

- [x] Unit tests pass
- [x] Integration tests pass
- [x] Verified fix resolves the Sentry error pattern

# Decisions made

- [Document any non-obvious choices]

# Checks

- [x] I have tested this code
- [x] I have reviewed my own PR
- [x] I have set a descriptive PR title compliant with conventional commits

# Reviewing tips

- Focus on [specific areas]
- The main change is in [file/function]

# User facing release notes

- Fixed: [user-visible description of what was broken]
EOF
)"

Sentry MCP Tools Reference

Based on Sentry MCP documentation (https://docs.sentry.io/product/sentry-mcp/):

Category	Tools
Core	Organizations, Projects, Teams, Issues, DSNs
Analysis	Error Searching, Issue Analysis, Seer Integration
Advanced	Release Management, Performance Monitoring, Custom Queries

Common Tool Patterns

# List organizations you have access to
mcp__Sentry__list_organizations

# List projects in an organization
mcp__Sentry__list_projects(organization_slug)

# Search for issues
mcp__Sentry__search_issues(organization_slug, query="is:unresolved")

# Get issue details
mcp__Sentry__get_issue(issue_id)

# Find errors in a specific file
mcp__Sentry__find_errors_in_file(organization_slug, project_slug, filename)

# Use Seer AI agent for analysis
mcp__Sentry__invoke_seer(issue_id)

Common Issue Patterns

Error Type	Typical Fix
NoneType errors	Add null checks, validate inputs
KeyError	Check dict key existence, use .get()
Timeout errors	Add retry logic, increase timeout
Connection errors	Add error handling, retry with backoff
Validation errors	Improve input validation, add type hints

Example Session

1. List organizations:
   > mcp__Sentry__list_organizations
   Result: [{"slug": "genlayer", ...}]

2. Search unresolved issues:
   > mcp__Sentry__search_issues(org="genlayer", query="is:unresolved", sort="freq")
   Result: Top issue: "KeyError: 'validator_address' in consensus/worker.py"

3. Get issue details:
   > mcp__Sentry__get_issue(issue_id="12345")
   Result: Stack trace showing error at worker.py:145

4. Analyze:
   - Error in worker.py:145 accessing result['validator_address']
   - Occurs when validator response is incomplete
   - 47 events, affecting 12 users

5. Plan:
   - Add validation before accessing key
   - Log warning for incomplete responses
   - Add unit test for edge case

6. Implement:
   - Edit worker.py to add .get() with default
   - Add test_worker_missing_validator_address test

7. Test:
   - gltest --contracts-dir . tests/unit/test_worker*.py
   - All pass

8. Create PR:
   - gh pr create with Sentry context

Troubleshooting

Sentry MCP Not Connected

• Restart Claude Code to trigger OAuth flow
• Check ~/.claude/settings.json has the mcpServers configuration
• Verify you have access to the Sentry organization

Cannot Find Issues

• Check organization and project slugs are correct
• Verify you have the right permissions in Sentry
• Try different query filters (is:unresolved, is:unhandled)

Cannot Reproduce Issue Locally

• Check environment differences (env vars, config)
• Use Sentry event data to understand exact conditions
• Add logging to capture more context

Tests Timeout

• Use --leader-only for faster integration tests
• Run specific test files instead of full suite