Port.io

When an incident fires, every minute of context-gathering is a minute you're not resolving. Port now supports the PagerDuty MCP Server so SREs can query live incident data from Port AI, grounded in your service catalog. No more jumping between PagerDuty and your IDP to figure out who owns the affected service, what the last deployment touched, or whether there's an open change request. The right context. The right team. Faster time to resolution. Learn more about Port’s new MCP Connector: https://lnkd.in/eiuZrfkS

Connecting a few MCP servers shouldn’t turn into infrastructure. But it does. Notion, Jira, GitHub, Dynatrace and suddenly you’re dealing with: - routing logic - OAuth per service - a gateway layer you didn’t plan to build This session is about getting back to simple. We’ll show how teams use Port to connect once, control access, and drop the routing layer entirely. Port Builders Session 🗓️April 15th, 10:00 AM EDT 🎤Matan Grady Cameron Kayfish Register here: https://lnkd.in/egtTszxV

Knowing a PR exists and knowing what's in it are two different things. Port now supports the GitHub MCP Server so developers can access pull request descriptions, comments, and commit history directly from Port AI, alongside the service ownership and scorecard context in Port's catalog. Platform engineers define exactly which GitHub tools are available. Read-only by default. Users authenticate with their GitHub credentials. Learn more about Port’s new MCP Connector: https://lnkd.in/eaHnesqx

While DORA metrics are the default for measuring velocity, they break in real-world orgs. Most orgs apply them the same way across every team, regardless of how those teams actually work. That’s where it breaks. Different teams ship differently. But they’re judged the same way. On April 8th, 2:00 PM EDT, John Crowley and Tomasz Skora are breaking down which engineering metrics actually drive outcomes. We’ll cover: • Why DORA breaks in real-world orgs • How to define metrics that match how your team actually works • How to roll it up without losing context Save your spot: https://lnkd.in/ev7APg-z

Join us to see Port’s AI capabilities, how they fit together, and what they unlock for platform teams. Matan Grady and Matar Peles will walk through it end to end. We’ll cover: - The new AI layer in Port, all rooted in context (core engine, agents, MCP, workflows, auto-discovery) - Autonomous ticket resolution, from triage to production - IDE-native workflows to query, act, and manage without leaving your tools March 30, 10:00 AM EDT Register here: https://lnkd.in/e35Eah8y

Port now supports the Atlassian Rovo MCP Server!  Teams that rely on Jira and Confluence can search, create, and update issues directly from Port AI without leaving their workflow. What this means for your teams: - “Show me all open P1 bugs for services my team owns". Port knows your team's services, Atlassian Rovo MCP searches Jira filtered to those projects. No need to remember which Jira projects map to which services. - Product managers can draft a spec in Port, publish it to Confluence, and break it into Jira epics and stories from a single chat prompt. Access is governed by your existing Atlassian permissions. Users see only what they already have access to. Learn more about Port’s MCP Connector: https://lnkd.in/e4Dqk7Qv

KubeCon Amsterdam, we’re here. 👋 Stop by Booth #330 to meet the team and see how Port helps engineering teams build autonomous workflows with the context and guardrails needed to stay in control. See you there!

MCP starts simple Then things get messy fast More tools More connections More to manage With Port MCP Connectors: - Bring external MCP servers into Port - Control access per team - One place for devs and agents to query everything Less to manage Read more on this blog by Tal Sabag and Stav Dlugovitzky : https://lnkd.in/d7vrMUwW Meet us at KubeCon Amsterdam this week if you want to see it live (Booth #330)

Support tickets are full of bug reports, edge cases, and customer pain points. They just live across Zendesk, Jira, Slack, and CRM notes. We built AI agents to make support tickets actually usable. Here’s what the agents do: - Summarize tickets into concise, useful context - Generate handovers with full timeline, repro steps, current hypotheses, and next steps - Format escalations with customer environment, integration version, and deployment details - Cluster tickets to show what a customer experienced over time - Analyze recurring tickets to surface systemic issues All tapping into Port’s Context Lake. Read more in this blog by Idit Matas & Oladipupo Ibeun: https://lnkd.in/eMUnaKWj