MCP Bitbucket

Overview

The MCP Bitbucket server brings the full power of Bitbucket’s REST API into a local, secure context that Claude and other AI assistants can call directly. By running the server on the same machine as an assistant, developers avoid exposing their credentials to external services while still enabling automated repository management, file manipulation, and issue tracking. This local bridge is especially valuable in regulated environments or on personal laptops where network policies restrict outbound traffic.

The server exposes a rich set of Bitbucket tools that mirror the most common Git‑hosting operations. With simple JSON payloads, an AI can create repositories, branches, and pull requests; read or update files; manage issues; and even delete resources when needed. Each tool requires only the essential parameters—such as , , or —and offers sensible defaults for workspace and branch names, making the interface straightforward for natural‑language commands. The design prioritizes clarity: tool names are prefixed with , and optional fields have clear fallbacks, reducing the cognitive load on developers who translate user intent into API calls.

Key capabilities include:

• Repository lifecycle: Create, search, and delete repositories with optional workspace scoping.
• Branch management: Spin up new branches from a chosen start point and delete them when they’re no longer needed.
• File operations: Read, write (create or update), and delete files directly in the repository’s tree.
• Issue handling: Add, delete, or search issues with fine‑grained control over kind and priority.
• Pull request orchestration: Open pull requests, set source/destination branches, and optionally close the source branch upon merge.

These features enable a wide array of real‑world workflows. For instance, an AI assistant can automatically scaffold a new project repository from a template, populate it with boilerplate code, and open an initial pull request for review—all in response to a single user query. In continuous‑integration pipelines, the assistant can trigger branch creation and PR submission after a successful test run, while also logging issues if tests fail. The server’s tight integration with Claude Desktop means that the assistant can present contextual prompts, fetch file contents for code review, or suggest issue titles based on commit messages without leaving the chat interface.

What sets this MCP server apart is its local, credential‑secure architecture. By running on the user’s machine and reading Bitbucket credentials from environment variables, it eliminates the need for cloud‑based intermediaries. The tooling is intentionally minimal yet comprehensive, covering almost all routine Bitbucket interactions with clear defaults and optional parameters. This balance of simplicity and power makes it an ideal addition to any developer’s AI toolkit, enabling automated codebase management while keeping sensitive data in‑house.