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One MCP connection. Full GitLab context. No more tab-switching — just ask.
Stop hunting through merge requests and pipeline logs to understand what's happening. Ask your AI agent for pipeline health, open MR status, commit history, failing jobs, and code review bottlenecks — across every project and group you own.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Create issues, trigger pipelines, assign merge requests, and manage labels — all through natural language. Eliminate the manual overhead of routine GitLab administration and project management.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set AI-powered watches on pipeline health, MR review velocity, and deployment frequency. Get proactive alerts when builds are consistently failing, review queues are backing up, or deployment cadence drops.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Create issues, trigger pipelines, assign merge requests, and manage labels — all through natural language. Eliminate the manual overhead of routine GitLab administration and project management.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
When a GitLab pipeline fails, engineers manually open the CI/CD log, scroll through hundreds of lines to find the error, check whether it's a flaky test or a real regression, and then decide whether to rerun or investigate. For teams running 50+ pipelines per day, this reactive loop consumes engineering hours that compound across the entire organization.
Ask your AI agent to triage pipeline failures automatically. It reads the failing job log, identifies the error type, checks whether similar failures occurred previously, and recommends whether to retry or investigate further — all in one prompt.
Merge requests sit in review queues for days without any systematic visibility. Engineering leads don't know which MRs are blocking other work, which reviewers are overwhelmed, or which PRs are close to merge but waiting on a minor comment reply. By the time someone notices a delivery milestone is at risk, the bottleneck has been building for a week.
Improvado MCP gives your AI agent a real-time view of the entire MR review queue. One prompt identifies which MRs are blocking downstream work, which reviewers have the most assigned, and which ones are closest to completion — so you can unblock delivery proactively.
Engineering managers overseeing multiple GitLab groups have to click through each project individually to assess sprint health, deployment frequency, and issue resolution rates. There is no cross-project view in GitLab by default — getting a portfolio-level picture means opening each project dashboard and manually aggregating numbers into a spreadsheet.
Ask your AI agent for a cross-project engineering health summary with one prompt. It aggregates pipeline pass rates, deployment frequency, MR cycle times, and issue velocity across all projects in your group — formatted for a leadership review.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
GitLab MCP is a Model Context Protocol server that connects your GitLab instance to AI agents like Claude, ChatGPT, and Gemini. It lets you query repositories, pipelines, merge requests, issues, and CI/CD data in natural language — and perform write actions like creating issues or triggering pipelines — without navigating the GitLab UI.
Repositories, branches, commits, merge requests, pipelines, CI/CD job logs, issues, milestones, labels, group and project members, deployments, and project health metrics. You can query across individual projects or aggregate across entire GitLab groups.
Both. Read operations include querying MR status, pipeline logs, commit history, and issue tracking. Write operations include creating issues, triggering pipelines, assigning MRs, updating labels, and managing milestones. Permissions are controlled by your GitLab personal access token scope.
Yes. Improvado MCP supports both GitLab.com and self-hosted GitLab instances (CE and EE). You configure the instance URL and API token during setup. Network accessibility from Improvado's proxy is required for self-hosted instances.
Yes. Improvado stores GitLab API tokens in an encrypted vault certified to SOC 2 Type II. The AI agent never accesses your credentials directly. All requests are proxied through Improvado's secure layer, and you control the token scope during setup.
Under 60 seconds. Add a GitLab personal access token with the appropriate scopes, configure the MCP server URL in your AI agent, and you're querying. Improvado users with GitLab already connected can start immediately.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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