Orchestrate GitHub to Vercel Releases via MCP.
Engineering ships 12 PRs per week but nobody can tell the PM which Linear tickets actually made it to production , the agent tracks every issue from backlog to deployment
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 current Linear sprint or cycle , which issues are in progress, done, or blocked. For each completed issue, it searches GitHub for the linked PR , is it open, in review, approved, or merged? For merged PRs, it checks Vercel , did the deployment succeed? Is the production build healthy? The agent builds a release status board: 'Issue LIN-234: Completed in Linear.
PR #891: Merged 2h ago. Vercel: Deployed to production , build healthy, no errors.' Or: 'Issue LIN-256: Done in Linear.
PR #903: Still in review , 2 change requests pending.' The PM sees exactly what shipped to production, what is stuck in code review, and what deployed but has issues.
No standup theater required.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Linear, GitHub and Vercel MCP servers so your AI agent tracks every engineering ticket from Linear backlog through GitHub PR to Vercel production deployment. Engineering and product teams who lose visibility between 'ticket closed' and 'actually deployed' get end-to-end release tracking , the agent knows which issues shipped, which are stuck in review, and which deployed but failed health checks.
Linear
triggerReads issues, sprint cycles, project status and team assignments
list_issues get_issue list_cycles list_projects Github
enrichmentTracks PRs linked to Linear issues, review status and merge state
list_pull_requests get_repository_details list_repo_issues get_file_contents Vercel
actionMonitors deployments, build status and production health for shipped features
list_deployments get_deployment_details list_projects get_project_details 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.
- Linear, Github & Vercel 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.
Product managers who need to know which Linear issues actually shipped to production this sprint
Engineering leads tracking PR-to-deploy pipeline velocity and identifying review bottlenecks
Release managers coordinating deployments who need to know which features are in each Vercel deployment
Teams doing continuous deployment who want automated release notes generated from merged issues
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: Linear, GitHub and Vercel. 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 MCP servers and paste a prompt.
Can I use Jira instead of Linear?
Yes. Swap the Linear MCP for the Jira Cloud MCP on Vinkius. Issue tracking concepts map directly , issues, sprints, and status work the same way.
Is my engineering data secure?
MCP servers authenticate through API keys. Linear, GitHub and Vercel data stays in your accounts. Vinkius does not store your issues, code or deployments.
Find API Vulnerabilities First Using MCP
Your OpenAPI spec has 14 security findings and 3 match active HackerOne reports , your agent creates the tickets before the bounty payout
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 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
MCP Recipe to Kill Codebase Bloat
Codebase audited, bloat identified, requirements questioned, lean tickets created , kill architectural complexity before it ships
MCP Servers for Multi-Client Sprint Management
Your dev team tracks their work in Linear but the PM reports to clients in ClickUp , which means every sprint update is manually transcribed between two tools, and by the time the client sees it in ClickUp the data is already outdated
MCP servers used in this workflow
Linear
Linear lets your AI client read, write, and manage issues directly inside Linear—no tab switching needed. You can list all teams, search for specific bugs, create new tasks with defined priorities, or add comments right from your IDE. It gives your agent full control over project metadata, allowing you to check sprint progress, view project scope, and audit issue status using 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.
Vercel
Vercel MCP Server lets your AI agent manage all deployment tasks directly in chat. You can list projects, trigger builds from a specific GitHub commit ref, check live build status, and audit custom domains—all without opening the Vercel web UI or clicking through dashboards.