MCP Recipe for Container Vulnerability Scanning.
Pipelines scanned, base images audited, vulnerability records created, remediation tracked , manage your container security without a CSPM tool
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads GitLab pipelines: the latest security scan for `api-server` flagged 3 vulnerabilities. It reads the Dockerfile from the repository , base image is `node:18.15-alpine`, published 14 months ago.
The latest `node:18` LTS is `18.20-alpine`. That is 5 minor versions behind. The agent checks Docker Hub: the production image `acme/api-server:v2.14.3` was built on this stale base.
For the `worker` service, the base image is `python:3.11.4-slim`, which is 8 months old , `3.11.9-slim` is current. It creates Airtable records: 'VUL-001: api-server base image 14 months stale (node:18.15 18.20).
Severity: High. Owner: @platform. Status: Open.' 'VUL-002: worker base image 8 months stale (python:3.11.4 3.11.9). Severity: Medium. Owner: @backend. Status: Open.' Each record has the image name, current version, latest version, days since last update, and a link to the GitLab pipeline.
The Airtable board becomes your security tracker , filter by severity, filter by owner, track resolution over time.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect GitLab, Docker Hub and Airtable Connectors so your AI agent reads your GitLab CI pipeline results, audits Docker Hub images for stale base images and known-vulnerable packages, and maintains a vulnerability tracking database in Airtable with severity, remediation status and owner. Security-conscious teams who cannot afford a full CSPM platform get a lightweight container security workflow. No enterprise security tooling required. One prompt and your container security posture is documented.
Gitlab
triggerReads CI pipeline security scan results and merge requests
list_project_pipelines list_merge_requests get_repository_file list_visible_projects Docker Hub
actionAudits image tags, base image age and repository metadata
list_tags get_tag get_repository list_repositories Airtable
actionMaintains vulnerability tracking records with remediation status
create_records list_records get_record list_bases 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.
- Gitlab, Docker Hub & Airtable 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 need container security auditing but cannot justify the cost of enterprise CSPM tools like Snyk or Prisma Cloud
Platform engineers responsible for base image updates who need automated staleness detection across all services
Compliance officers who need documented vulnerability tracking with remediation timelines for audit purposes
Small security teams who need a lightweight vulnerability management workflow without building a custom dashboard
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: GitLab, Docker Hub and Airtable. 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 GitHub instead of GitLab?
Yes. Replace the GitLab Connector with the GitHub Connector. The agent reads Dockerfiles and CI results from GitHub instead.
Does this replace a vulnerability scanner like Snyk?
It complements scanners by providing base image staleness detection and remediation tracking. For CVE-level scanning, pair with a dedicated scanner.
Can I use Google Sheets instead of Airtable?
Yes. Replace the Airtable Connector with Google Sheets. You lose the structured database view but gain spreadsheet flexibility.
How often should I run the audit?
Weekly is a good cadence. Base images do not change daily, but vulnerabilities are disclosed regularly. A weekly audit catches new staleness before it becomes critical.
Debug CI Pipeline Failures Faster Using MCP
Your CI pipeline takes 47 minutes and nobody knows which step is the bottleneck , your AI agent analyzes every build, identifies the slow steps, and posts a weekly efficiency report
Connectors That Auto-Generate Pipeline Docs
Pipeline fails tracked, documentation cross-checked, team notified , engineering visibility without status meetings
Track Engineering Metrics Using Connectors
Merge request velocity measured, pipeline success rates tracked, cycle time calculated, team metrics published , build your DORA dashboard without a BI tool
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
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
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
Connectors used in this workflow
GitLab
GitLab MCP. It connects your GitLab instance to your AI agent so you can manage issues, track merge requests, and monitor CI/CD pipelines directly from your chat. It's built for teams that want to automate DevSecOps workflows and keep project data in sync without leaving their primary workspace.
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.
Airtable
Airtable MCP connects your Airtable bases to your AI agent. It lets you query records, edit spreadsheets, and build automated workflows through chat. Instead of clicking through rows, you just tell your agent what to do.