Integrations & MCP connectors
Why data ingestion matters
Engineering Intelligence relies on connected data from across your toolchain. The more sources you connect, the richer the context available to dashboards, scorecards, and AI agents, the more accurately Port can link metrics to the services, teams, and owners they belong to.
Without integrated data, metrics live in silos: deployment frequency in one tool, incident data in another, PR throughput in a third. Port brings these together into a single catalog, so you can answer questions like "which team has the highest change failure rate and the slowest review times?" questions that no single tool can answer alone.
What Port ingests and why
| Category | Examples | What it enables (examples) |
|---|---|---|
| Version control | GitHub, GitLab, Azure DevOps, Bitbucket | DORA metrics, delivery performance tracking, delivery bottlenecks, PR staleness, service production readiness scorecard, code standards enforcement |
| Issue tracking | Jira, Linear, Azure DevOps Boards | Lead time tracking, alignment between engineering output and business priorities |
| Incident management | PagerDuty, OpsGenie, Incident.io | MTTR tracking, MTBF calculations, reliability analysis |
| CI/CD & deployment | GitHub Actions, GitLab CI, Azure Pipelines, ArgoCD | Pipeline reliability scorecards, deployment frequency metrics |
| Observability | Datadog, New Relic, Dynatrace | Operational context like SLO compliance and service health |
| AI coding tools | GitHub Copilot, Claude, Cursor | AI adoption tracking and ROI analysis |
| Security & code quality | Snyk, SonarQube, Wiz, Checkmarx | Security data, production readiness and security compliance scorecards |
| Cloud & infrastructure | AWS, Azure, GCP, Kubernetes | Service dependencies, deployment targets, blast radius analysis |
Full integrations catalog
For the complete list of supported integrations, configuration details, and setup instructions, see the Port integrations catalog.
Don't see your tool? Port's Ocean custom integration framework lets you build production-grade integrations for any data source, on-premises systems, legacy tools, internal APIs, or proprietary platforms that don't have out-of-the-box integrations. This means Engineering Intelligence isn't limited to SaaS tools with public APIs; you can ingest data from across your entire estate, regardless of where it lives or how old it is.
Port also supports lightweight integrations via webhooks and a flexible API for simpler use cases.
MCP connectors
Beyond data ingestion, Port's MCP connectors allow AI agents to query data from external tools in real time without needing to ingest it into the software catalog & context lake first. This means Engineering Intelligence insights can draw from your entire organization context, enriching agent recommendations with live data.
Available MCP connectors include GitHub, GitLab, Atlassian, Linear, Slack, PagerDuty, Sentry, New Relic, Dynatrace, Notion, HubSpot, and custom remote MCP servers for proprietary tools.
See Conversational Insights to learn how AI agents, MCP connectors, and natural language queries work together to turn your context lake into actionable engineering intelligence.