AI Agents Drastically Cut Developer Portal Setup Time with Port's MCP

Setting up a comprehensive Internal Developer Portal (IDP) typically demands weeks of effort, involving intricate configurations of blueprints, actions, workflows, GitHub integrations, and API calls. A recent demonstration, however, revealed a paradigm shift: an AI agent, leveraging Port’s MCP and a single, concise prompt, fully configured an empty Port instance in mere minutes. The agent performed environment discovery, created essential blueprints for clusters, namespaces, and custom CRDs (apps, databases), deployed a Kubernetes exporter via Argo CD, and established GitHub blueprints. Crucially, it registered six self-service actions (create, update, delete for apps and databases), each wired to corresponding GitHub Actions workflows, automating resource creation and lifecycle management. While a few direct API calls were necessary for actions not yet covered by MCP tools, the overwhelming majority of the setup was orchestrated by the AI through Port’s 27 powerful MCP tools.

Beyond rapid setup, the integration significantly enhances the platform user experience. Users can query app statuses or initiate resource creation interactively through a coding agent like Claude Code, bypassing traditional UIs and dashboards. This streamlined interaction triggers Port actions, GitHub workflows, and Argo CD synchronizations, abstracting complex backend processes. Port’s strategic investment in its data lake, often referred to as the “Context Lake,” as a single source of truth, combined with its dynamic and extensive API, underpins this AI capability, allowing agents to understand and operate within the portal’s specific configuration. Despite these advancements, areas for improvement include enhancing the MCP with inherent AI intelligence for easier prompting by newcomers, better auto-discovery for Kubernetes resources, and streamlining custom prompt integration. A critical suggested enhancement involves Port providing hints to agents about external runtime data sources (Kubernetes, AWS, GitHub) to enable AI to access more detailed, real-time operational context, potentially transforming the portal into an ‘everything portal’.