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
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.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect GitLab, CircleCI and Google Sheets Connectors 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 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, Circleci & Google Sheets 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 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 Connector Orchestration
Which Connectors 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 Connector with the GitHub Connector. 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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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.
CircleCI
CircleCI MCP lets you manage CI/CD pipelines through your AI agent. You can trigger builds, check job statuses, and audit workflows without switching tabs. It connects your CircleCI account to your agent so you can handle deployment tasks using natural language.
Google Sheets
Google Sheets MCP lets you read, write, and manage spreadsheet data through your AI agent. Stop wasting time on manual data entry or complex formulas. Just tell your agent to pull specific ranges, add new rows, or create entire new sheets on the fly. It handles the tedious work of keeping your data organized so you can focus on making decisions.