Connectors to Find Abandoned Docker Images.
Your production image is 2.3GB and nobody knows why , it was 400MB two years ago but 47 engineers added 'just one more dependency' and now your deploy takes 12 minutes to pull
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent queries Docker Hub for all repositories and their tags , image sizes, creation dates, tag naming patterns, and the size evolution over time.
It identifies trends: 'payment-api image size: v1.0 (380MB), v1.5 (620MB), v2.0 (1.1GB), v2.3 (2.3GB). Growth rate: 6x in 18 months.' Then it reads the Dockerfile from GitHub to diagnose why: multi-stage build? Base image choice? Dependency bloat? Leftover build artifacts? The agent reports: 'Base image: node:20 (1.1GB) instead of node:20-slim (180MB).
That is 900MB of unnecessary OS packages. Build artifacts: node_modules included dev dependencies (puppeteer adds 300MB). Debug tools: curl, vim, htop installed in production image , 120MB of tools nobody uses in production.' The Discord report shows the size trend, the diagnosis, and a concrete optimization plan: 'Switch to slim base, multi-stage build, prune dev deps.
Estimated new size: 340MB (85% reduction). Deploy pull time: 2 minutes instead of 12.'
Connector Orchestration: 3 Connectors, one intelligent agent
Connect Docker Hub, GitHub and Discord Connectors so your AI agent reads your Docker Hub repositories and image tags, cross-references them with the Dockerfiles and CI configuration in GitHub, and posts container health intelligence to Discord. Teams whose container images have grown from 400MB to 2.3GB over two years , because every engineer adds dependencies but nobody removes them , and whose deploy times have tripled without anyone connecting image size to deployment performance , get archaeological analysis of their container history with actionable cleanup recommendations.
Docker Hub
triggerReads repository metadata, image tags, tag history and size trends
list_repositories get_repository list_tags get_tag Github
enrichmentReads Dockerfiles, CI configs and dependency files to identify bloat sources
get_file_contents search_github_code get_repository_details list_pull_requests Discord
actionPosts container health reports with size trends, bloat analysis and optimization roadmaps
create_message list_guild_channels get_channel 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.
- Docker Hub, Github & Discord 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.
Platform engineers tracking container image size growth who need tag-by-tag archaeology of what caused bloat
DevOps teams optimizing deploy times who need to connect image size to pod startup latency
Engineering leads conducting infrastructure audits who want to quantify the cost of container bloat in dollars and time
Teams running Kubernetes with autoscaling who need faster image pulls to survive traffic spikes
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: Docker Hub, GitHub and Discord. Connect all three to your AI client before running any prompt from this page.
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. Connect the Connectors and paste a prompt.
We use a private registry, not Docker Hub. Does this work?
This recipe uses the Docker Hub MCP. If your private registry has an Connector, the same analysis logic applies. The Dockerfile analysis via GitHub works regardless of where images are stored.
Is my container data secure?
Connectors authenticate through API keys. Docker Hub and GitHub data stays in your accounts. The agent reads image metadata and Dockerfiles, not your container contents. Vinkius does not store your data.
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
MCP Recipe for Container Vulnerability Scanning
Pipelines scanned, base images audited, vulnerability records created, remediation tracked , manage your container security without a CSPM tool
MCP Workflow for Container Build Monitoring
Pipelines monitored, build times tracked, image sizes audited, flaky steps flagged , keep your CI healthy without watching build logs
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 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
Generate Error Postmortems Automatically via MCP
Errors captured, stack traces analyzed, root cause commits identified, postmortem docs generated , write incident reports without the pain
Connectors used in this workflow
Docker Hub
Docker Hub lets you manage your container images through your agent. You can list repositories, browse specific tags, create new hosts, and search the community registry for the right base images. It handles the heavy lifting of navigating the Docker Hub website, letting you focus on deployment and configuration.
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.
Discord
Discord MCP lets you manage your community directly through your AI agent. You can list channels, send messages, moderate content, and audit members without leaving your chat interface. It turns your AI into a power user for your Discord community.