Generate Error Postmortems Automatically via MCP.
Errors captured, stack traces analyzed, root cause commits identified, postmortem docs generated , write incident reports without the pain
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads Honeybadger: 3 unresolved faults this week. Fault #4821 , `NoMethodError: undefined method 'charge' for nil:NilClass` , 847 occurrences, first seen June 2 at 03:14 UTC.
The agent reads the stack trace: `app/services/billing_service.rb:42`. It checks Honeybadger deployments: deploy `v3.8.1` went out at 02:58 UTC , 16 minutes before the first occurrence.
The agent goes to GitHub: PR #312 merged at 02:45 UTC, 'refactor(billing): extract payment processor' by @carlos. Changed files include `billing_service.rb`.
The agent reads the diff , line 42 used to call `@processor.charge(amount)` but the refactor changed the initialization, and `@processor` is nil when the Stripe adapter is not configured.
Root cause identified. The agent creates a Notion postmortem: 'Incident: Billing charge failures. Duration: 6h 12m. Impact: 847 failed charges ($23,400 revenue at risk).
Root cause: PR #312 removed processor initialization guard. Fix: PR #315 restored nil check. Action items: 1) Add integration test for nil processor.
2) Add Honeybadger alert threshold for billing errors.' The postmortem is done before the retro meeting starts.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Honeybadger, GitHub and Notion MCP servers so your AI agent reads production errors from Honeybadger, traces each fault to the responsible commit in GitHub, and generates a structured postmortem document in Notion with timeline, root cause, impact and action items. Engineering teams that dread writing postmortems get them auto-generated with real data. No reconstructing timelines from memory. No guessing which deploy caused the error. One prompt and the postmortem is drafted.
Honeybadger Error Tracking
triggerReads production faults, notices and deployment history
list_faults get_fault list_notices list_deployments get_notice Github
actionTraces errors to commits and changed files
list_pull_requests get_file_contents search_github_code get_repository_details Notion
actionCreates structured postmortem pages in the engineering wiki
create_page query_database search_pages get_database 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.
- Honeybadger Error Tracking, Github & Notion 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.
Engineering teams who delay writing postmortems because the timeline reconstruction is painful and time-consuming
SRE teams who need auto-generated incident reports with root cause attribution for compliance and audit requirements
Engineering managers who want postmortem quality to be consistent across teams, not dependent on individual writing skills
Startup teams without a dedicated SRE who need incident documentation without the overhead of a formal process
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: Honeybadger, GitHub and Notion. 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 I use Bugsnag instead of Honeybadger?
Yes. Replace the Honeybadger MCP server with the Bugsnag MCP server. Both provide error tracking with deployment correlation.
Does the agent resolve faults automatically?
The agent analyzes and documents faults. Resolving them in Honeybadger requires the resolve_fault tool. The workflow focuses on postmortem generation.
How accurate is the root cause attribution?
The agent correlates deployment timestamps with error onset and file-level changes. It flags probable causes based on timing and file overlap. Final verification is always human.
Can I customize the postmortem template?
Yes. Describe your preferred structure in the prompt: 'Use our template: Summary, Timeline, Five Whys, Action Items, Customer Communication.' The agent adapts.
MCP Recipe to Fix Production Crashes Faster
Your app crashed 847 times yesterday and the error report sits in Honeybadger while your Linear board has no idea , the engineer who wrote the broken code merged a different PR today
Prioritize Errors by Revenue Impact via MCP
Two crashes happened at 3am , one affects a $500/month customer and the other affects a $180K/year enterprise account , but your PagerDuty treats them identically because error monitoring has no idea how much money is at risk
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
How MCP Servers Auto-Triage Bug Reports
New bugs detected, severity classified, sprint tickets created, team notified , triage your backlog without a standup
MCP servers used in this workflow
Honeybadger (Error Tracking)
Honeybadger (Error Tracking) MCP Server monitors your application's health and exceptions. You can list all monitored projects, check uptime status across sites, and query fault groups to diagnose issues. It lets you deep-dive into individual errors (notices) or mark faults as resolved, all through natural conversation.
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.
Notion
Notion MCP Server connects your AI client to the entire Notion workspace. It lets you query structured databases, search pages across titles and content, and read deep into nested document blocks—all through a single API layer. Don't copy-paste data or switch tabs; let your agent act as an intelligent librarian for all your wiki entries and project trackers.