confluence-mcp-server

# Confluence MCP Server [](https://mcpreview.com/mcp-servers/pawankumar94/confluence-mcp-server) 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. Built with Flask for easy deployment to Cloud Run. ## Features - 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 ## Installation 1. Clone the repository 2. Install dependencies: ```bash pip install -r requirements.txt ``` ## Configuration Create a `.env` file in the project root with the following variables: ```env CONFLUENCE_URL=https://your-instance.atlassian.net/wiki CONFLUENCE_ACCESS_TOKEN=your_access_token PORT=8080 # Optional, defaults to 8080 ``` To get an access token: 1. Log in to your Atlassian account 2. Go to Account Settings > Security > Create and manage API tokens 3. Create a new API token and copy it ## Available Tools The server provides the following MCP tools: ### 1. Search Content ```python @tool("search_confluence") def search(query: str) -> Dict[str, Any] ``` ### 2. Get Spaces ```python @tool("get_spaces") def get_spaces() -> Dict[str, Any] ``` ### 3. Get Page Content ```python @tool("get_page_content") def get_page_content(space_key: str, page_id: str) -> Dict[str, Any] ``` ### 4. Create Page ```python @tool("create_page") def create_page(space_key: str, title: str, content: str) -> Dict[str, Any] ``` ### 5. Update Page ```python @tool("update_page") def update_page(space_key: str, page_id: str, content: str) -> Dict[str, Any] ``` ### 6. Delete Page ```python @tool("delete_page") def delete_page(space_key: str, page_id: str) -> Dict[str, Any] ``` ## Running Locally Run the server locally: ```bash python example.py ``` The server will start on http://localhost:8080 ## Cloud Run Deployment 1. Build the Docker image: ```bash docker build -t confluence-mcp . ``` 2. Tag and push to Google Container Registry: ```bash docker tag confluence-mcp gcr.io/[PROJECT-ID]/confluence-mcp docker push gcr.io/[PROJECT-ID]/confluence-mcp ``` 3. Deploy to Cloud Run: ```bash gcloud run deploy confluence-mcp \ --image gcr.io/[PROJECT-ID]/confluence-mcp \ --platform managed \ --allow-unauthenticated \ --set-env-vars="CONFLUENCE_URL=[YOUR_URL],CONFLUENCE_ACCESS_TOKEN=[YOUR_TOKEN]" ``` ## Error Handling All tools include proper error handling and will return appropriate error messages in the response. The response format includes: - Success case: Relevant data in the specified format - Error case: `{"error": "error message"}` ## Security Considerations 1. Always use environment variables for sensitive data 2. Consider using Cloud Run's built-in secret management 3. Implement proper authentication for your endpoints 4. Keep your Confluence access token secure ## Contributing Contributions are welcome! Please feel free to submit a Pull Request. ## License This project is licensed under the MIT License - see the LICENSE file for details.