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.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect GitHub, Checkly and Better Stack MCP servers 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 MCP
Turn any internal API into an MCP server. 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 every 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 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure 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 every platform.
Contextual Reasoning
Every 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 MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog 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 MCP Server Orchestration
Which MCP servers 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 MCP server with GitLab. The agent reads merge requests instead of pull requests for root cause tracing.
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MCP servers used in this workflow
Checkly
Checkly connects your AI agent directly to application monitoring and E2E testing data. You can list all configured API endpoint checks, run immediate system health tests, and get performance metrics on demand—all through natural conversation. It handles everything from uptime tracking to auditing alert channels.
Better Stack
Better Stack connects your infrastructure monitoring and incident management to your AI agent. Use the Better Stack MCP Server to list, create, and manage all your HTTP, Ping, and Keyword monitors. You can also get real-time updates on active incidents, check on-call schedules, and verify public status pages directly from your chat client.
GitHub
GitHub MCP Server manages repositories, tracks issues, and searches code via AI agents. Connect your GitHub account to your preferred AI client and automate core developer workflows—listing repos, getting file contents, or creating new issues—all from a natural conversation. Manage your entire software development lifecycle without leaving your chat window.