MCP Recipe for Pre-Mortem System Analysis.
Architecture red-teamed, failure modes quantified, monitoring alerts created , pre-mortem your system before production breaks it
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads the GitHub repository: a payments service connecting to PostgreSQL via PgBouncer with a 200-connection pool, processing Stripe webhooks with a 30-second gateway timeout.
The agent runs `validate_inversion_thinking`. Hypothesis: 'The payment service handles 5,000 transactions per second with p99 latency under 200ms.' The agent then inverts: Anti-Pattern , single PostgreSQL instance, no circuit breaker, synchronous webhook processing.
Each request holds a database connection for the full gateway timeout. Red Team Attack , deterministic, not probabilistic: at Stripe gateway p99 of 2.5 seconds during Black Friday, each transaction holds a connection for 2.5s.
At 240 TPS: 240 2.5 = 600 connections needed. Pool capacity: 200. Connection pool WILL exhaust at 80 TPS during gateway degradation.
Kill Criteria: PgBouncer pool utilization above 90% for 30 consecutive seconds. Defense: async webhook processing via Redis Streams, circuit breaker with 50% failure threshold.
Post-Mortem: the async defense introduces eventual consistency , users see 'processing' instead of immediate confirmation, and if Redis restarts without AOF persistence, unprocessed payments are lost.
The agent then creates Datadog monitors: 'pgbouncer.pools.server_active / pgbouncer.pools.server_total > 0.9' with P1 alert, 'stripe.webhook.latency.p99 > 2500ms' with P2 warning, 'redis.streams.pending_entries > 1000' for the new async defense.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect GitHub, Inversion Thinking Prover and Datadog Connectors so your AI agent reads your codebase architecture, runs every critical service through a six-pivot red-team simulation demanding measurable kill criteria and second-order failure prediction, and creates Datadog monitors for every failure mode identified. Engineering teams shipping without pre-mortem analysis get an automated adversarial audit that defines exactly how the system will break, at what threshold, and what monitoring must exist to catch it. No guesswork. No vague risk assessments. One prompt and your agent destroys its own hypothesis, quantifies the failure, and deploys the monitoring to detect it.
Github
triggerReads service architecture, connection configurations, and infrastructure manifests
get_file_contents search_github_code list_user_repositories Inversion Thinking Prover
actionRuns six-pivot red-team simulation demanding measurable kill criteria and second-order failures
validate_inversion_thinking Datadog
actionCreates monitors and alerts for every failure mode identified by the red-team simulation
create_monitor search_monitors get_monitor 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.
- Github, Inversion Thinking Prover & Datadog 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.
SRE teams preparing for traffic spikes who need pre-mortem analysis with automatically configured monitoring for every identified failure mode
Engineers shipping new services to production who need adversarial validation of their architecture before the first deployment
Platform teams responsible for payment infrastructure who cannot afford connection pool exhaustion or webhook processing failures during peak load
Teams adopting async processing patterns who need monitoring for the new failure modes introduced by eventual consistency and queue-based architectures
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: GitHub, Inversion Thinking Prover and Datadog. 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.
What makes this different from a standard monitoring setup?
Standard monitoring watches generic metrics. This workflow derives monitors from specific, mathematically proven failure scenarios. Each alert exists because a red-team attack proved the failure will happen at a specific threshold.
Does the agent actually create Datadog monitors?
Yes. The agent uses the Datadog Connector to create fully configured monitors with query syntax, thresholds, and alert escalation policies derived from the red-team analysis.
How often should I run this pre-mortem?
Before every major deployment, infrastructure change, or expected traffic spike. Run it when you change database configurations, add new services, or prepare for seasonal events.
Can it analyze multiple services at once?
Yes. The agent processes each service independently, running separate red-team simulations and creating service-specific monitors. A full platform audit typically takes 5-10 minutes.
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Connectors used in this workflow
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.
Inversion Thinking Prover
Inversion Thinking Prover forces your AI agent to stop being a yes man. It applies Charlie Munger's inversion principle to your technical plans, making your agent hunt for failure modes, define measurable kill criteria, and simulate post-mortems before a single line of code is written. It is a sanity check for high-stakes architecture.
Datadog
Datadog MCP. It lets you query performance metrics, search application logs, and manage active monitors directly from your AI agent. You can check system health, find specific error traces, and audit your alerting rules without ever leaving your chat or IDE. It turns your observability data into a conversational tool for faster troubleshooting and more efficient incident response across your entire cloud infrastructure.