Confluence MCP Server
by pawankumar94
A Model Context Protocol (MCP) server implementation for Atlassian Confluence. This server provides a set of tools for interacting with Confluence through the MCP protocol, allowing AI agents to seamlessly work with Confluence content.
Last updated: N/A
What is Confluence MCP Server?
The Confluence MCP Server is a Flask-based server that implements the Model Context Protocol (MCP) for interacting with Atlassian Confluence. It provides a set of tools that enable AI agents to seamlessly access and manipulate Confluence content.
How to use Confluence MCP Server?
To use the server, you need to clone the repository, install the dependencies, configure the environment variables (Confluence URL and access token), and then run the server locally or deploy it to Cloud Run. The server exposes various MCP tools that can be called to perform actions like searching, creating, reading, updating, and deleting Confluence pages.
Key features of Confluence MCP Server
Search pages and spaces using Confluence Query Language (CQL)
List all available Confluence spaces
Create, read, update, and delete Confluence pages
Rich metadata support for Confluence resources
Flask-based server for Cloud Run deployment
MCP tools for AI agent integration
Use cases of Confluence MCP Server
Enabling AI agents to search and retrieve information from Confluence
Automating the creation and updating of Confluence pages
Integrating Confluence content into AI-powered workflows
Building AI-driven chatbots that can answer questions based on Confluence content
FAQ from Confluence MCP Server
What is MCP?
What is MCP?
MCP stands for Model Context Protocol. It's a protocol designed to allow AI agents to interact with various data sources and applications.
How do I get a Confluence access token?
How do I get a Confluence access token?
Log in to your Atlassian account, go to Account Settings > Security > Create and manage API tokens, and create a new API token.
Can I deploy this server to other platforms besides Cloud Run?
Can I deploy this server to other platforms besides Cloud Run?
Yes, since it's a Flask-based server, you can deploy it to any platform that supports Python and Flask, such as AWS Elastic Beanstalk or Heroku.
What happens if a tool encounters an error?
What happens if a tool encounters an error?
All tools include proper error handling and will return an error message in the response in the format: {"error": "error message"}.
How can I contribute to this project?
How can I contribute to this project?
Contributions are welcome! Please feel free to submit a Pull Request.