Atlassian Bitbucket MCP Server

The Atlassian Bitbucket MCP Server bridges AI assistants with a Bitbucket Cloud instance, turning the version‑control system into an interactive data source for natural‑language queries. By exposing Bitbucket’s APIs through the Model Context Protocol, it lets models such as Claude or Cursor AI retrieve real‑time repository information, inspect pull requests, and explore workspaces without leaving the chat interface. This eliminates the tedious copy‑paste workflow that developers normally perform when they need to reference code or review changes.

Developers benefit from a minimal‑input, maximal‑output design: each tool requires only the essential identifiers—workspace slug, repository slug, or pull‑request ID—and returns a richly detailed JSON payload. For example, requesting yields the repository’s description, language distribution, owner details, and direct URLs to the web UI. This level of detail allows AI assistants to understand context instantly, enabling advanced code‑review suggestions, automated documentation generation, or compliance checks that are grounded in the latest repository state.

Key capabilities include:

• Workspace discovery () and detailed workspace metadata (), letting users navigate multi‑tenant environments.
• Repository enumeration () and deep inspection (), providing insights into project structure, language usage, and access links.
• Pull‑request management ( and ) with optional state filtering, enabling AI to surface open reviews, merged history, or pending approvals.

Real‑world use cases span from continuous integration—where an AI assistant can automatically flag style violations before a PR is merged—to knowledge transfer, where new team members ask the model to list all repositories in a workspace and receive an instant, navigable overview. In security audits, the assistant can pull all open PRs and highlight those that modify critical files, facilitating rapid triage.

Integration into existing AI workflows is straightforward: the MCP server acts as a trusted proxy, authenticating via Atlassian API tokens or Bitbucket app passwords. The AI client sends a tool invocation, the server queries Bitbucket, and the enriched response feeds back into the conversation. This tight coupling preserves data confidentiality while granting developers powerful, context‑aware insights directly within their preferred chat environment.