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.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect Honeybadger, GitHub and Notion Connectors 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 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.
- Honeybadger Error Tracking, Github & Notion 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.
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 Connector Orchestration
Which Connectors 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 Connector with the Bugsnag Connector. 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.
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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
How Connectors Auto-Triage Bug Reports
New bugs detected, severity classified, sprint tickets created, team notified , triage your backlog without a standup
Connectors used in this workflow
Honeybadger (Error Tracking)
Honeybadger (Error Tracking) MCP lets you manage app exceptions and uptime directly through your AI client. List projects, analyze fault groups, check site availability, and track recent deployments without switching tabs. It's built for developers who need to stay on top of production issues in real-time.
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.
Notion
Notion MCP lets your AI agent read, search, and update your Notion workspace. It turns static docs into a live database your agent can query to find project specs, manage tasks, and update team pages instantly. This makes your company knowledge searchable and actionable from any MCP-compatible client.