MCP Recipe for Faster Incident Response.
Endpoints monitored, failures detected, incidents auto-created, root cause traced to the commit , respond to outages before users tweet
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent triggers Checkly synthetic checks on your 6 critical endpoints. The `/api/auth/login` endpoint returns 500 , it was returning 200 fifteen minutes ago.
The agent pulls performance history: response time spiked from 120ms to timeout at 30s. It checks the other 5 endpoints , all healthy.
Isolated failure in auth service. The agent creates a Better Stack incident: Severity 1, Service: auth-service, Title: '/api/auth/login returning 500 , started 14:47 UTC.' Then it traces the cause: the agent pulls the latest merged PRs from GitHub for the auth-service repo.
PR #198 'refactor(auth): migrate to JWT v9' was merged at 14:42 UTC , 5 minutes before the failure. The PR changed `src/middleware/auth.ts` and `src/lib/jwt.ts`.
The agent adds this to the incident notes: 'Probable cause: PR #198 by @carlos , JWT library migration. Files changed: auth.ts, jwt.ts.
Merged 5 minutes before failure onset.' The on-call engineer sees the incident, the affected file, and the exact commit. Time from detection to actionable incident: 47 seconds.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect GitHub, Checkly and Better Stack Connectors so your AI agent runs synthetic checks on your critical endpoints via Checkly, detects failures, creates an incident in Better Stack with severity and affected service, and traces the root cause back to the last GitHub deployment commit. On-call engineers get a complete incident package , what broke, when, which commit caused it , in under 60 seconds from detection. No manual correlation. No 'let me check when the last deploy was.' One prompt and the incident response is underway.
Checkly
triggerRuns synthetic checks and detects endpoint failures
list_checkly_checks trigger_check_run get_check_details get_check_performance_metrics Better Stack
actionCreates and manages incidents with severity classification
list_incidents list_monitors get_incident create_monitor Github
actionTraces failures back to recent commits and deployments
list_pull_requests get_repository_details get_file_contents search_github_code Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own Connector
Convert any internal API into a Connector. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Checkly, Better Stack & Github ready in the catalog right now
- Add more from 5,800+ servers whenever you need
- Connections are secured and compliant by default
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added weekly
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 5,800+ Connectors and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across all tools, in one conversation. That's what this connectivity layer was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across all platforms.
Contextual Reasoning
Each decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your Connectors once, and your agent handles the rest. Each time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius AI Connectors to handle these for you. You describe what you need. It does the rest.
On-call engineers who need incident context within 60 seconds , what broke, when, and which commit likely caused it
SRE teams running Checkly synthetic monitoring who want auto-generated Better Stack incidents with root cause attribution
Small teams without a dedicated SRE who need an automated incident response workflow that does the correlation work for them
Engineering managers who need incident post-mortem data (timeline, root cause, affected services) automatically documented
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: Checkly, Better Stack and GitHub. Connect all three to your AI client.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others.
Can this replace my existing alerting?
It complements it. Checkly and Better Stack handle detection and notification. This workflow adds automated root cause correlation that traditional alerting lacks.
How fast is the detection-to-incident pipeline?
Under 60 seconds from Checkly detecting a failure to a fully contextualized Better Stack incident with probable root cause.
Does it support multi-region checks?
Yes. Checkly runs checks from multiple regions. The agent reports which regions are affected , a failure in US-East but not EU-West suggests a regional infrastructure issue.
Can I use GitLab instead of GitHub?
Yes. Replace the GitHub Connector with GitLab. The agent reads merge requests instead of pull requests for root cause tracing.
Catch Frontend Downtime Early Using Connectors
Your landing page passed the Lighthouse audit but your checkout flow takes 11 seconds in Brazil because nobody runs synthetic checks from outside us-east-1
Track Frontend Performance Budgets via MCP
Deploys tracked, performance budgets enforced, Lighthouse scores logged, regressions flagged , guard your Core Web Vitals without manual audits
Connectors That Correlate Incidents Across Tools
OpsGenie wakes your on-call engineer at 3am, Better Stack shows the uptime check failed, and nobody knows if this is the same incident that happened last Tuesday or something new
Deploy Containers to Production Using MCP
Code pushed, images built, tags verified, deploys triggered, status reported , ship containers from commit to production in one prompt
Extract Architecture Principles Using MCP
Code patterns formalized, universal laws derived, causal forces identified , replace ad-hoc architecture with mathematical proof
Find Codebase Duplications Using Connectors
Your codebase has 4 different implementations of date formatting, 3 versions of the retry logic, and 2 competing validation libraries , but nobody knows because grep only finds exact matches and these duplicates are semantic
Connectors used in this workflow
Checkly
Checkly MCP lets you manage your application monitoring and synthetic testing through your AI client. You can track API uptime, trigger manual checks, and audit your alert channels without leaving your chat interface. It connects your monitoring stack directly to your workspace for faster responses to system issues and better visibility into your backend health.
Better Stack
Better Stack MCP. Monitor uptime and incidents directly from your AI agent. List monitors, heartbeats, and on-call schedules without switching tabs. Get real-time updates on active incidents, manage your status pages, and verify your cron job heartbeats through natural conversation. It's the fastest way to keep your infrastructure oversight organized.
GitHub
GitHub MCP lets you manage your entire software development lifecycle through a chat interface. You can check the status of a pull request, list open issues, or search for specific code snippets without ever leaving your primary workspace. It gives your AI agent direct access to your repositories, making it easier to audit codebases or update project statuses on the fly.