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Improvado's MCP server connects Jenkins pipeline data to your AI agent. Ask about build failures, deployment frequency, test flakiness, and job health — without trawling through Jenkins logs manually. Works with Claude, Cursor, and any MCP-compatible tool.
Stop clicking through nested job views and scrolling through build logs. Ask your AI agent about failure rates by pipeline, flaky test patterns, build duration trends, or which jobs are blocking deployments.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Trigger builds, restart failed stages, update job configurations, and manage queue priorities directly through your AI agent. Operational pipeline tasks without touching the Jenkins UI.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set up AI-powered watches on build success rates, queue depth, and deployment frequency. Get notified when pipelines degrade before they block your release cycle.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Trigger builds, restart failed stages, update job configurations, and manage queue priorities directly through your AI agent. Operational pipeline tasks without touching the Jenkins UI.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
You have 15 tests that fail intermittently — not reliably, just often enough to make engineers click 'retry' on autopilot. Real failures get dismissed as 'probably flaky.' A genuine regression gets retried twice and merged anyway. The flaky test problem is known but nobody has time to analyze which tests are actually problematic.
Your AI agent analyzes build history across all pipelines and surfaces tests ranked by failure inconsistency — high failure rate but not 100% failure, which is the flaky fingerprint. It also shows which flaky tests correlate with real build failures that got merged. Prioritize which to fix with data.
Six months ago, your main pipeline took 12 minutes. Now it takes 28 minutes. It happened incrementally — each change added 30 seconds, nobody flagged it. Now developer feedback loops are slow and deployment frequency has dropped. Finding which stages got slower requires digging through months of build logs manually.
Ask your AI agent to compare build stage durations over time. It identifies which specific stages slowed down, correlates with the date when changes were introduced, and surfaces the top contributors to build time regression.
You want to measure DORA metrics for your engineering team, but deployment frequency data is scattered across dozens of Jenkins jobs. Some deploy to staging, some to production. Some run on merge, some on schedule. Aggregating this into a coherent deployment frequency metric requires custom scripting that nobody owns.
Improvado's MCP server can query across all Jenkins jobs simultaneously. Ask for deployment frequency by service and environment, filtered to production jobs only. Get DORA-ready metrics in natural language without building a data pipeline.
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.
Build history, job status, pipeline stage results, test reports, build parameters, queue state, and job configurations. You can query across all jobs or drill into specific pipelines and date ranges.
Both. The MCP server works with freestyle jobs, Pipeline jobs, Multibranch Pipelines, and folder-organized job structures. Stage-level data is available for Declarative and Scripted Pipeline jobs that use Jenkins' standard stage reporting.
Both read and write are supported. You can trigger builds with parameters, restart failed stages, abort running builds, enable or disable jobs, and update build retention policies — all through your AI agent.
The MCP server queries the Jenkins API with filtering so it doesn't pull all job data on every request. When you ask about specific pipelines or failure patterns, it fetches only the relevant subset. Query performance scales well even on large Jenkins instances with 500+ jobs.
Improvado MCP connects to Jenkins using API tokens stored in an encrypted secrets vault — credentials are never exposed in query results or logs. Access is scoped to read-only endpoints by default, so your pipeline configurations and build artifacts remain protected. You can revoke or rotate tokens at any time without reconfiguring the integration.
Yes. Improvado MCP can pull data from multiple Jenkins controllers into a unified dataset, letting AI agents compare build success rates, average job durations, and failure patterns across all instances in a single query. This is especially useful for platform engineering teams managing many separate pipelines and wanting a consolidated health view.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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