Polarion MCP Server

Overview

Polarion MCP Servers provide a dedicated bridge between the Polarion Application Lifecycle Management (ALM) platform and AI assistants that consume Model Context Protocol (MCP). By exposing a rich set of MCP tools, the server allows AI agents to query and manipulate Polarion work items, documents, spaces, and custom fields directly from the assistant’s context. This eliminates the need for developers to write custom integrations or maintain separate API wrappers, enabling rapid prototyping of AI‑powered workflow assistants.

The server solves the problem of data silos in ALM environments. Polarion stores requirements, defects, test cases, and associated artifacts behind a web interface that is not designed for programmatic consumption. Polarion MCP Servers translate these resources into MCP‑compatible endpoints, making them first‑class citizens in the assistant’s knowledge graph. Developers can then ask an AI to “list all defects with severity high” or “retrieve the text of a specific requirement”, and the assistant can fetch that data in real time, without manual export or database access.

Key features of the Polarion MCP Servers include:

• Work‑item retrieval – pulls the main description for any set of work‑item IDs, while extracts configured custom attributes such as priority or defect type.
• Document navigation – Tools like , , and allow the assistant to explore project documentation, filter by title or space, and locate work items referenced within documents.
• Project configuration – The server supports multiple Polarion projects in a single instance, each identified by an alias. Configuration files map project URLs, authentication credentials, and custom field mappings, enabling the assistant to switch context seamlessly.
• SSE‑based streaming – The variant uses Server‑Sent Events to push updates, ensuring that the assistant receives real‑time changes without polling.

Real‑world use cases abound. A release manager can ask an AI to “generate a risk summary for the next sprint” and receive up‑to‑date defect counts and severity distributions. A quality engineer can query “list all test cases linked to requirement X” and immediately see the latest status. In continuous integration pipelines, an AI bot could automatically create or update Polarion work items based on build results, using the MCP tools to write back changes.

Integration into AI workflows is straightforward: an MCP client (such as Claude or OpenAI’s new tool‑use feature) connects to the server via a project‑specific URL, then invokes the exposed tools as part of its prompt. Because the server handles authentication and data mapping internally, developers can focus on crafting conversational logic rather than plumbing. The standout advantage of Polarion MCP Servers is their single‑source‑of‑truth approach—AI assistants operate on live Polarion data, eliminating stale exports and ensuring compliance with governance policies.