Bitbucket Pipelines MCP Server

The Bitbucket Pipelines MCP Server bridges the gap between AI assistants and continuous‑integration workflows on Bitbucket. By exposing a set of well‑defined tools through the Model Context Protocol, it allows language models such as Claude to query pipeline status, trigger new runs, list existing pipelines, and halt running jobs—all without leaving the conversational interface. This eliminates the need for developers to manually open the Bitbucket UI or run CLI commands, streamlining feedback loops during code reviews and feature development.

At its core, the server implements four primary tools. The list pipelines tool retrieves a paginated view of all pipelines in a repository, enabling the assistant to provide up‑to‑date overviews or filter by branch. The trigger pipeline tool accepts a rich target specification—including reference type, name, and optional selector patterns—alongside custom variables, giving developers the ability to spin up targeted builds from natural language prompts. The get pipeline status tool fetches the current state of a specific run by UUID, allowing real‑time monitoring or status checks during debugging sessions. Finally, the stop pipeline tool lets users abort a run that is no longer needed or has stalled. These capabilities translate directly into common CI/CD tasks such as “run a build on the develop branch,” “what’s the status of my last pipeline?” or “stop the current build.”

The server is designed for seamless integration into existing AI workflows. Developers can simply add a new entry in the configuration of their Cursor environment, pointing to the Docker container that hosts the MCP server. Once configured, the assistant can invoke any of the Bitbucket tools with a single keyword—. This pattern keeps context clean, reduces boilerplate, and ensures that the AI remains stateless while still performing powerful actions against external services.

Real‑world scenarios abound. A developer asking the assistant to “run a pipeline on the feature branch and notify me when it completes” will have the server trigger the build, poll for status updates, and return a concise summary. In code‑review mode, an assistant can list all pipelines that affected the current pull request, helping reviewers gauge build health before merging. For operations teams, the ability to stop runaway pipelines from a chat window speeds incident response and saves compute costs. The server’s pagination support also means that large repositories with many pipelines can be navigated efficiently, keeping responses snappy and relevant.

Unique to this MCP implementation is its tight coupling with Bitbucket’s REST API, including optional custom variable injection and selector patterns for granular target selection. This gives users more control than generic CI tools, allowing sophisticated branching strategies and environment‑specific variables to be managed directly through conversational commands. Combined with the simplicity of Docker deployment and a clear, schema‑driven toolset, the Bitbucket Pipelines MCP Server offers developers an intuitive, AI‑powered interface to their CI/CD pipelines.