Feature ideas

Support running Port Agent in an unprivileged Docker context, similar to how Ocean exporters are configured. Currently, Ocean exporter images are set up with a non-root user (ocean, uid/gid 999), which allows operation in restricted environments. In contrast, when inspecting the Port Agent image ( ghcr.io/port-labs/port-agent:v0.8.0 ), the USER field is not set, and the default CMD runs commands that may require root permissions. Providing an unprivileged Port Agent image would ensure better compatibility with environments where root access is restricted.

Gain practical insights from dashboard widgets (e.g., line charts) using AI to support organizational improvement.

When integrating with Port Agents via API, it would be helpful to be able to pass in the context of a request for a particular user. In general, there should be a mechanism to allow authorization as a particular user from a third party system to enable building custom integrations. Our use case is specifically related to SSO-based auth as well.

Use AI to create dashboards in Port without writing code

Bring your own MCP. Port’s AI today only sees what’s inside Port. By bringing in a customer's MCP Server and their AI Agents, they are already pulling in external context from Confluence, Jira, and other systems. Their AI Agents may have deep context surrounding their business. Customer wants Port to be able to tap into that external intelligence so the AI inside Port can act with full awareness of both Port and non-Port data.

The local python MCP supports passing CLIENT_ID and CLIENT_SECRET, but the port hosted MCP server requires the use of a browser and cookies to authenticate. We would like to use similar flow to the API server to talk to the MCP (exchange CLIENT_ID and CLIENT_SECRET for an access token and use access token in the Authorization header).

Get AI-assistance when building calculation property, reducing the complexity of writing JQ manually