Which integrations are available for this server?

Enables interaction with the Atlassian ecosystem by connecting to Confluence Cloud or Server instances for documentation and workspace management. Provides tools to search for pages, blog posts, and attachments, fetch full page content with HTML-to-Markdown conversion, and list all pages within a specific workspace.

How do I use Confluence MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Confluence MCP Server search for the project onboarding documentation" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Confluence MCP Server

A Model Context Protocol (MCP) server that bridges Confluence Wiki with Large Language Models, enabling LLMs to search, read, and explore Confluence content seamlessly.

Features

MCP Protocol Compliance: Implements the official Model Context Protocol with stdio communication
Three Core Tools:
- search_confluence: Search for pages, blog posts, and attachments
- read_page: Fetch full page content with automatic HTML-to-Markdown conversion
- list_space_content: List all pages within a Confluence workspace
Smart Content Conversion: Automatically converts Confluence HTML to clean Markdown for optimal LLM context usage
Robust Error Handling: Gracefully handles authentication (401) and not found (404) errors
Docker Support: Optional containerization for easy deployment

Prerequisites

Python 3.11 or higher
uv (recommended) or pip
Confluence Cloud or Server instance
Confluence API token (generate from your Atlassian account settings)
Docker (optional, for containerized deployment)

Quick Start

1. Clone and Setup

git clone <repository-url> cd conflience-mcp-test

2. Configure Credentials

Create a .env file from the example:

cp .env.example .env

Edit .env with your Confluence credentials:

CONFLUENCE_URL=https://your-domain.atlassian.net CONFLUENCE_USERNAME=your-email@example.com CONFLUENCE_API_TOKEN=your-api-token-here

3. Install Dependencies

Using uv (recommended)

# Install uv if you haven't already curl -LsSf https://astral.sh/uv/install.sh | sh # Sync dependencies and create virtual environment uv sync # Activate the virtual environment source .venv/bin/activate

Using pip

pip install -r requirements.txt

4. Test the Server

Option A: Using the built-in host client

Run the host client to test the MCP server:

# If using uv, prefix commands with 'uv run' or activate the venv first uv run python host.py search_confluence '{"query": "project documentation"}' # Or with activated venv python host.py search_confluence '{"query": "project documentation"}' python host.py read_page '{"page_id": "123456"}' python host.py list_space_content '{"space_key": "TEAM"}'

Option B: Using MCP Inspector (Recommended for Development)

The MCP Inspector is the official testing tool for MCP servers.

Install MCP Inspector:

# TypeScript or Javascript npx @modelcontextprotocol/inspector node path/to/server/index.js args... #Python npx @modelcontextprotocol/inspector \ uv \ --directory path/to/server \ run \ package-name \ args...

Configure the server: The Inspector runs directly through npx without requiring installation:

# Copy the example config cp mcp-config.example.json mcp-config.json # Edit mcp-config.json with your Confluence credentials

The config should look like:

{ "mcpServers": { "confluence": { "command": "uv", "args": ["run", "python", "server.py"], "env": { "CONFLUENCE_URL": "https://your-domain.atlassian.net", "CONFLUENCE_USERNAME": "your-email@example.com", "CONFLUENCE_API_TOKEN": "your-api-token" } } } }

Launch the Inspector:

mcp-inspector mcp-config.json

This will open a web interface at http://localhost:5173 where you can:

View all available tools
Test each tool with custom inputs
See real-time request/response data
Debug tool execution

Note: You can also use environment variables from .env by modifying the config to load from the file.

For a comprehensive testing guide including troubleshooting and advanced usage, see TESTING.md.

Docker Deployment

The Docker container runs the MCP server with HTTP/SSE transport (Server-Sent Events), making it accessible over the network.

Build the Image

docker build -t confluence-mcp .

Run the Container

# Run with environment file docker run --rm -p 8000:8000 --env-file .env confluence-mcp # Or pass environment variables directly docker run --rm -p 8000:8000 \ -e CONFLUENCE_URL="https://your-domain.atlassian.net" \ -e CONFLUENCE_USERNAME="your-email@example.com" \ -e CONFLUENCE_API_TOKEN="your-token" \ confluence-mcp

Connect to the Docker Container

Once the container is running, use the host client with the --http flag:

# Test from host machine python host.py --http http://localhost:8000 search_confluence '{"query": "test"}' python host.py --http http://localhost:8000 read_page '{"page_id": "123456"}' # Test from Docker container python host.py --http http://localhost:8000/sse list_space_content '{"space_key": "TEAM"}' # Automatically uses "my-server" if it's the only one npx @modelcontextprotocol/inspector --config mcp-config.json --server confluence-sse

Architecture:

Server Transport: HTTP/SSE (Server-Sent Events over port 8000)
Client Connection: Uses --http flag to connect via HTTP
Benefits: Can be accessed remotely, scales horizontally, works with load balancers

Architecture

This project implements the Model Context Protocol (MCP), which allows LLMs to interact with external tools and data sources. The server supports two transport modes:

Local Mode (stdio)

┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ LLM Client │◄───────►│ MCP Server │◄───────►│ Confluence │ │ (host.py) │ stdio │ (server.py) │ API │ Wiki │ └─────────────────┘ └──────────────────┘ └─────────────────┘

Docker Mode (HTTP/SSE)

┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ LLM Client │◄───────►│ MCP Server │◄───────►│ Confluence │ │ (host.py) │HTTP/SSE │ (Docker) │ API │ Wiki │ │ --http flag │ :8000 │ (server.py) │ │ │ └─────────────────┘ └──────────────────┘ └─────────────────┘

Transport Options:

stdio: For local development and testing
HTTP/SSE: For containerized deployment, remote access, and production use

API Tools

search_confluence

Search for content across your Confluence instance.

Input:

{ "query": "search terms" }

Output:

{ "results": [ { "title": "Page Title", "type": "page", "id": "123456" } ] }

read_page

Fetch the full content of a Confluence page, converted to Markdown.

Input:

{ "page_id": "123456" }

Output:

{ "title": "Page Title", "content": "# Markdown content here...", "page_id": "123456" }

list_space_content

List all pages within a specific Confluence space.

Input:

{ "space_key": "TEAM" }

Output:

{ "space_key": "TEAM", "pages": [ { "title": "Page Title", "id": "123456" } ], "total": 42 }

Development

Project Structure

server.py - MCP server implementation using FastMCP
host.py - MCP client for testing
pyproject.toml - Project metadata and dependencies (uv configuration)
requirements.txt - Python dependencies (for pip compatibility)
Dockerfile - Container configuration
.env.example - Environment variables template
mcp-config.example.json - MCP Inspector configuration template
func_req.md - Functional requirements specification
TESTING.md - Comprehensive testing guide with troubleshooting

Common uv Commands

# Install dependencies uv sync # Run server locally (stdio mode) uv run python server.py # Run server with HTTP/SSE mode uv run python server.py --transport sse --host 0.0.0.0 --port 8000 # Run client (stdio mode) uv run python host.py search_confluence '{"query": "test"}' # Run client (HTTP mode) uv run python host.py --http http://localhost:8000 search_confluence '{"query": "test"}' # Add a new dependency uv add package-name # Remove a dependency uv remove package-name # Update all dependencies uv sync --upgrade # Show installed packages uv pip list

Requirements Alignment

This implementation follows the requirements specified in func_req.md:

✅ REQ-01: Confluence API integration with search, read, and list tools
✅ REQ-01: HTML to Markdown conversion for LLM context efficiency
✅ REQ-01: Error handling for 401 and 404 responses
✅ REQ-02: Docker containerization with stdio communication
✅ REQ-02: Environment-based configuration
✅ REQ-03: Simple MCP host client with CLI interface

Contributing

This project follows PEP 8 coding conventions. When contributing:

Ensure all three tools maintain backward compatibility
Add appropriate error handling for new API calls
Update documentation for new features
Test with both local and Docker deployment

License

[Your License Here]

Confluence MCP Server

Confluence MCP Server

Features

Prerequisites

Quick Start

1. Clone and Setup

2. Configure Credentials

3. Install Dependencies

Using uv (recommended)

Using pip

4. Test the Server

Option A: Using the built-in host client

Option B: Using MCP Inspector (Recommended for Development)

Docker Deployment

Build the Image

Run the Container

Connect to the Docker Container

Architecture

Local Mode (stdio)

Docker Mode (HTTP/SSE)

API Tools

search_confluence

read_page

list_space_content

Development

Project Structure

Common uv Commands

Requirements Alignment

Contributing

License

Resources

confluence-mcp-container

Resources

Tools

New MCP Servers

Latest Blog Posts

MCP directory API