Track Engineering Metrics Using MCP Servers.
Merge request velocity measured, pipeline success rates tracked, cycle time calculated, team metrics published , build your DORA dashboard without a BI 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 the last 7 days of merge requests from GitLab: 34 MRs merged across 6 projects. Average time from MR creation to merge: 18.3 hours.
That is your lead time. It checks CircleCI: 156 pipeline runs this week, 142 passed, 14 failed. Change failure rate: 9.0%.
Average pipeline duration: 8.2 minutes. It calculates deployment frequency: 34 deploys in 7 days = 4.9 per day , elite performance per DORA standards.
It cross-references the 14 failures with the MRs that triggered them: 6 were flaky tests (same test failed and passed on retry), 5 were genuine code issues, 3 were infra timeouts.
The agent writes this to your Google Sheet: Week 23 row with all metrics, team breakdown, trend arrows versus last week.
Lead time improved 12% (was 20.8h). Change failure rate up 2 points (was 7%). Deployment frequency flat. The spreadsheet accumulates weekly data.
After 3 months, you see the trendlines without touching a chart tool.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect GitLab, CircleCI and Google Sheets MCP servers so your AI agent pulls merge request data from GitLab, pipeline execution stats from CircleCI, and computes DORA-style engineering metrics , deployment frequency, lead time for changes, change failure rate, and time to restore. Results are written to a Google Sheet that serves as your living engineering dashboard. Engineering leaders who need weekly metrics without building a custom BI pipeline get the numbers automatically. No Looker setup. No data warehouse.
Gitlab
triggerReads merge requests, pipeline history and project metadata
list_merge_requests list_project_pipelines get_project_details list_visible_projects Circleci
actionPulls pipeline run times, success rates and failure details
list_cci_pipelines list_pipeline_workflows list_workflow_jobs get_job_details Google Sheets
actionWrites weekly metrics to a shared engineering dashboard
update_sheet_values append_sheet_values create_spreadsheet get_spreadsheet 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.
- Gitlab, Circleci & Google Sheets 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 managers who need weekly DORA metrics for leadership reports without building a custom analytics pipeline
VP of Engineering teams tracking engineering velocity across multiple squads who need a shared, auto-updating dashboard
Startups preparing for due diligence who need to demonstrate engineering maturity with quantifiable delivery metrics
Platform teams investigating CI/CD bottlenecks who need pipeline failure analysis with flaky test detection
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: GitLab, CircleCI and Google Sheets. 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.
What are DORA metrics?
DORA (DevOps Research and Assessment) metrics are four key indicators of software delivery performance: deployment frequency, lead time for changes, change failure rate, and mean time to restore service.
Can I use GitHub instead of GitLab?
Yes. Replace the GitLab MCP server with the GitHub MCP server. The agent reads pull requests instead of merge requests , the metric calculations remain the same.
How accurate is the lead time calculation?
The agent measures from MR/PR creation timestamp to merge timestamp. This captures code review time and CI wait time. It does not include pre-development planning time.
Can I add custom metrics beyond DORA?
Yes. Tell the agent in your prompt: 'Also track average PR review time, number of review comments, and hotfix ratio.' The agent will compute and add these columns.
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MCP servers used in this workflow
GitLab
GitLab MCP Server connects your entire development ecosystem to your AI client. Use it to list projects, check CI/CD pipeline status, track open issues, and read file contents across your entire GitLab instance. It lets your agent manage the full DevSecOps lifecycle—from initial issue creation to final deployment—all via natural conversation. It's your central hub for project metadata and code visibility.
CircleCI
CircleCI MCP Server lets your AI agent manage your entire CI/CD lifecycle. Use it to list recent pipelines, check job statuses, trigger new builds, and audit project workflows without opening the CircleCI dashboard. It gives you full control over your software delivery process directly from your chat client.
Google Sheets
Google Sheets MCP Server lets your AI client read, write, and manage data directly in Google Sheets. Use conversational commands to pull data from specific ranges, append new rows, or structure entire spreadsheets. It acts as an analyst, letting you manipulate complex data without opening the GUI or writing formulas.