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.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect GitHub, Inversion Thinking Prover and Datadog MCP servers 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 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.
- Github, Inversion Thinking Prover & Datadog 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.
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 MCP Server Orchestration
Which MCP servers 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 MCP server 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.
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 MCP Servers
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
Generate Error Postmortems Automatically via MCP
Errors captured, stack traces analyzed, root cause commits identified, postmortem docs generated , write incident reports without the pain
How MCP Servers Auto-Triage Bug Reports
New bugs detected, severity classified, sprint tickets created, team notified , triage your backlog without a standup
MCP Recipe for Code Review Time Analytics
Review bottlenecks detected, unreviewed PRs surfaced, reviewer workload balanced, team velocity measured , fix your code review process with data
MCP servers used in this workflow
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.
Inversion Thinking Prover
The Inversion Thinking Prover's `validate_inversion_thinking` tool forces your AI agents to think like adversaries. It prevents sycophancy by requiring a six-pivot cognitive trap: defining the core hypothesis, identifying the exact anti-pattern, simulating a Red Team attack, setting measurable kill criteria, designing a defense, and finally, predicting how that defense fails. This ensures your architectural plans are brutally stress-tested before they ship.
Datadog
Datadog connects your AI agent directly to your infrastructure monitoring stack. Query performance metrics, search logs for specific errors, and check system monitor status using natural conversation. You get real-time visibility into application health without opening a dashboard.