Confluence MCP Server

Overview

The Spring Boot AI Confluence MCP Server is a ready‑to‑deploy service that bridges an AI assistant with Atlassian Confluence Cloud. By exposing Confluence operations as MCP tools, it lets developers treat the collaboration platform as a first‑class data source for conversational agents. This solves the common pain point of having to write custom connectors or REST wrappers for every new knowledge base: the server automatically translates tool calls into authenticated Confluence API requests, returning structured results that can be fed back into the model’s context.

At its core, the server implements a set of annotated methods marked with . Each method corresponds to a specific Confluence action—such as listing all spaces, retrieving page content, or creating a new document. The registers these methods with the MCP framework so that any compatible client (Claude, Gemini, or a custom agent) can discover and invoke them via the standard MCP protocol. The integration handles authentication, pagination, and error mapping behind the scenes, allowing developers to focus on business logic rather than API plumbing.

Key capabilities include:

• Space and page management: Enumerate spaces, fetch page hierarchies, and create or update pages directly from the assistant.
• Content retrieval: Pull page bodies in Markdown, HTML, or storage format for analysis or summarization.
• History and versioning: Access revision histories to track changes or retrieve previous states.
• Search and filtering: Query pages by labels, titles, or content to surface relevant information quickly.

Real‑world scenarios benefit from this server in several ways. A knowledge‑base chatbot can pull the latest product documentation on demand, automatically update a troubleshooting guide when new issues are reported, or generate meeting minutes by creating pages from conversation transcripts. In a development pipeline, an AI assistant could scan Confluence for architecture diagrams or compliance documents before initiating code changes. Because the tools are exposed through MCP, any client that understands the protocol can tap into Confluence without writing new connectors.

The server’s design offers distinct advantages. Built on Spring Boot AI, it inherits robust dependency injection, security features, and a familiar development environment for Java/Kotlin teams. The use of annotations keeps the code declarative and concise, while the MCP framework guarantees consistent request handling and response formatting. Additionally, the server is fully testable with existing MCP clients like the Claude desktop app, enabling rapid iteration and validation of tool functionality. Overall, this MCP server provides a seamless, low‑overhead pathway for integrating Confluence Cloud into AI workflows.