MCP Atlassian Server

Atlassian MCP Server (by phuc‑nt) is a Model Context Protocol (MCP) implementation that bridges AI assistants—such as Cline, Claude Desktop, or Cursor—to the core Atlassian ecosystem. By exposing Jira and Confluence as first‑class resources and tools, the server removes the friction of manual navigation and allows an AI agent to query issues, create tickets, manage sprints, edit pages, or post comments—all through a unified, language‑agnostic interface. For developers who spend time juggling project tracking and documentation, this server turns routine interactions into conversational commands, freeing cognitive bandwidth for higher‑level problem solving.

At its core, the server offers a clean separation between read‑only resources and actionable tools. Resources provide structured data such as Jira issues, projects, users, Confluence spaces, and pages, while tools expose mutations like creating an issue, transitioning a ticket, or adding a Confluence comment. This duality aligns with the MCP design philosophy: read operations are safe and idempotent, whereas write operations carry explicit intent. The result is a predictable API surface that AI assistants can explore and invoke without compromising data integrity.

Key capabilities include:

• Full Atlassian API coverage: The server has been updated to use Jira API v3 and Confluence API v2, ensuring compatibility with the latest platform features.
• Agile workflow support: Advanced sprint and board management tools allow an AI to plan, update, or close sprints directly from the chat.
• Rich Confluence handling: Page versioning, attachment uploads, and comment threading are exposed as tools, enabling dynamic documentation workflows.
• Local‑first architecture: Designed for personal development environments, the server can run locally and sync with on‑prem or cloud instances of Jira/Confluence.
• MCP Marketplace integration: The server is listed in the MCP marketplace, making discovery and onboarding for clients like Cline effortless.

Real‑world scenarios abound. A product manager could ask the AI to “create a new Jira issue for the bug found in page X,” while a technical writer might request, “add a comment to the Confluence page ‘Release Notes’ with this summary.” In a distributed team, an AI could automatically sync sprint backlogs from Jira into Confluence dashboards or generate status reports that combine issue metrics and documentation snippets—all without leaving the assistant’s conversation.

Because it adheres strictly to MCP standards, the server can be plugged into any compliant AI client. Developers benefit from a single point of integration: one service that translates natural language commands into authenticated API calls, handles pagination, and formats responses for the assistant. The result is a seamless workflow where AI becomes an extension of Atlassian, rather than a separate tool that requires context switching.