Azure DevOps MCP

The Azure DevOps MCP server is a purpose‑built bridge that lets AI assistants like Claude interact with Azure DevOps services without the friction of manual authentication or project switching. In a typical development environment, teams often work across multiple Azure DevOps organizations and projects, each with its own Personal Access Token (PAT). Managing these tokens in environment variables or a single configuration file can become unwieldy and error‑prone. This MCP solves that problem by tying the authentication context directly to the file system: as soon as the AI assistant changes its working directory, the server automatically reads a local file and authenticates against the corresponding organization, project, and PAT. The result is a seamless, zero‑configuration experience where the assistant always talks to the right Azure DevOps instance.

At its core, the server exposes a rich set of Azure DevOps capabilities—work items, repositories, builds, pull requests, and pipelines—through the MCP interface. Developers can ask the AI to list open work items, create a new branch, trigger a build pipeline, or review recent pull requests—all without leaving the chat. Because each repository can enable or disable specific tools via the section of its configuration, teams retain fine‑grained control over what data the assistant can access. The server also handles token storage securely: PATs are kept in repository‑level files that should be excluded from version control, and the MCP falls back gracefully to environment variables if a local config is missing.

Real‑world scenarios for this server are plentiful. A DevOps engineer might ask the AI to “show me all work items assigned to me in the current project” while working on a feature branch. A release manager could request “trigger pipeline X and wait for completion” without manually opening the Azure portal. When a developer switches from the RiverSync project to Mula, the assistant instantly shifts context, eliminating cross‑project credential mistakes. The server’s robust error handling and retry logic also mean that transient network issues or API rate limits are managed transparently, keeping the AI workflow uninterrupted.

Integration into existing AI workflows is straightforward: the MCP server registers itself with Claude’s MCP registry, and any assistant that supports MCP can automatically discover and invoke its tools. Because the server is written in Node.js, it runs on any platform that supports the MCP protocol, making it a versatile addition to CI/CD pipelines, IDE extensions, or standalone command‑line tools. Its unique advantage lies in the combination of directory‑based context switching, secure per‑repo token storage, and a comprehensive set of Azure DevOps APIs—all delivered through a single, lightweight MCP server.