# Confluence RAG Data Pipeline with MCP Protocol
A Model Context Protocol (MCP) server that provides relevant context from Confluence pages using RAG (Retrieval Augmented Generation).
[](https://badge.fury.io/py/confluence-scraper-mcp)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
## 🚀 Quick Start
```bash
# Install from PyPI
pip install confluence-scraper-mcp
# Set environment variables
export CONFLUENCE_BASE_URL="https://your-domain.atlassian.net"
export CONFLUENCE_TOKEN="your-api-token"
export CONFLUENCE_SPACE_KEY="your-space-key"
# Run as MCP server
confluence-scraper-mcp
# Or run as web server
confluence-scraper-mcp --web
```
## Features
- 🔍 **Semantic Search**: Uses ChromaDB for vector-based document retrieval
- 🔗 **MCP Integration**: Full Model Context Protocol implementation
- 📚 **Confluence Native**: Direct integration with Confluence API
- 🏷️ **Smart Filtering**: Filter by spaces, labels, and metadata
- 📎 **Rich Content**: Handles attachments and comments
- 🌐 **Dual Mode**: Run as MCP server or REST API
- 📦 **Easy Install**: Available on PyPI
## Requirements
- Python 3.9 or higher
- Confluence API access token
- ChromaDB for vector storage
## Installation
1. **Install from PyPI (Recommended):**
```bash
pip install confluence-scraper-mcp
```
2. **Install UV if you haven't already:**
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
3. **Clone and Setup Project (Development):**
```bash
git clone <repository-url>
cd confluence-scraper-mcp
# Create virtual environment
uv venv .venv
# Activate virtual environment
source .venv/bin/activate
# Install dependencies
uv pip install -r requirements.txt
```
4. **Configure Environment:**
- Create a `.env` file in the project root:
```bash
touch .env
```
- Add the following configuration (adjust values as needed):
```bash
# Required settings
CONFLUENCE_BASE_URL=https://your-domain.atlassian.net
CONFLUENCE_TOKEN=your-api-token
CONFLUENCE_SPACE_KEY=optional-space-key
# Optional settings (with defaults)
INITIAL_CRAWL=false
CHROMA_PERSIST_DIR=./data/chroma
EMBEDDING_MODEL="all-MiniLM-L6-v2"
MAX_PAGES=1000
INCLUDE_ATTACHMENTS=true
INCLUDE_COMMENTS=true
```
## Usage
### Command Line Interface (After PyPI Installation)
```bash
# Run as MCP server (stdio mode) - default
confluence-scraper-mcp
# Run as web server
confluence-scraper-mcp --web
```
### Development Mode
1. **Using uvx (Recommended):**
```bash
# Development mode with auto-reload
uvx uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
# Run tests
uvx pytest
# Code formatting and checks
uvx black .
uvx isort .
uvx mypy .
```
2. **Alternative: Using Virtual Environment:**
```bash
# Activate virtual environment
source .venv/bin/activate
# Then run commands as usual
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
```
3. **Initial Setup:**
```bash
# Start initial crawl of Confluence pages
curl -X POST http://localhost:8000/crawl
# Verify server health
curl http://localhost:8000/health
```
4. **Use the MCP API:**
```bash
# Get context for an LLM query
curl -X POST http://localhost:8000/mcp/context \
-H "Content-Type: application/json" \
-d '{
"messages": [{"role": "user", "content": "Tell me about project X"}],
"query": "project X documentation",
"max_context_length": 1000
}'
# The response will include relevant context from your Confluence pages
```
5. **Monitor and Maintain:**
```bash
# View logs
tail -f logs/app.log
# Re-crawl Confluence (e.g., after updates)
curl -X POST http://localhost:8000/crawl
```
## API Endpoints
- `GET /health`: Health check endpoint
- `POST /crawl`: Trigger Confluence crawl
- `POST /mcp/context`: Get relevant context for a query
## MCP (Model Context Protocol) Configuration
This server implements the Model Context Protocol (MCP) for seamless integration with AI assistants and LLM clients.
### Quick MCP Setup
1. **Install the package:**
```bash
pip install confluence-scraper-mcp
```
2. **Copy the MCP configuration:**
```bash
# Copy the example configuration
cp examples/mcp-client-config.json ~/.config/your-mcp-client/
```
3. **Update environment variables in the config:**
```json
{
"mcpServers": {
"confluence-scraper-mcp": {
"command": "confluence-scraper-mcp",
"args": [],
"env": {
"CONFLUENCE_BASE_URL": "https://your-domain.atlassian.net",
"CONFLUENCE_TOKEN": "your-api-token",
"CONFLUENCE_SPACE_KEY": "your-space-key"
}
}
}
}
```
### MCP Tools Available
The server provides several MCP tools:
- **`confluence_search`**: Search Confluence pages using semantic search
- **`confluence_get_page`**: Retrieve specific page content by ID or title
- **`confluence_crawl`**: Trigger crawling and indexing of content
### Example MCP Tool Usage
```json
{
"method": "tools/call",
"params": {
"name": "confluence_search",
"arguments": {
"query": "API authentication methods",
"space_key": "DEV",
"max_results": 3,
"include_attachments": true
}
}
}
```
### MCP Configuration Files
The package includes example configuration files:
- **`examples/mcp.json`**: Complete MCP server specification
- **`examples/mcp-client-config.json`**: Simple client configuration
See the [MCP specification](https://spec.modelcontextprotocol.io/) for more details on the protocol.
## 🤖 GitHub Copilot Integration
### Quick Setup for Copilot
1. **Install the package:**
```bash
pip install confluence-scraper-mcp
```
2. **Configure VS Code Settings:**
Open VS Code settings (`Cmd+,`) and add to your `settings.json`:
```json
{
"github.copilot.chat.mcpServers": {
"confluence-rag": {
"command": "confluence-scraper-mcp",
"args": [],
"env": {
"CONFLUENCE_BASE_URL": "https://your-domain.atlassian.net",
"CONFLUENCE_TOKEN": "your-api-token",
"CONFLUENCE_SPACE_KEY": "your-space-key"
}
}
}
}
```
3. **Initial Setup:**
```bash
# Start server and crawl content
confluence-scraper-mcp --web &
curl -X POST http://localhost:8000/crawl
```
4. **Test with Copilot:**
Open Copilot Chat and ask: *"How do we handle authentication in our system?"*
### Detailed Setup Guide
For complete setup instructions, see: **[📖 Copilot Setup Guide](docs/COPILOT_SETUP.md)**
## Using with Code Assistants
This MCP server specializes in Confluence documentation and uses RAG (Retrieval Augmented Generation) with ChromaDB:
**Key Features:**
- 🔗 **Confluence Integration**: Direct API integration with page, attachment, and comment handling
- 🔍 **Semantic Search**: ChromaDB vector search for meaning-based retrieval
- 🏷️ **Smart Filtering**: Filter by space keys, labels, content types
- 📊 **Metadata Preservation**: Maintains Confluence structure and relationships
```json
{
"endpoints": [
{
"name": "API Documentation",
"url": "http://localhost:8000/mcp/context",
"options": {
"max_context_length": 2000,
"filter": {
"space_key": "API",
"labels": ["technical-docs", "api-reference"],
"include_comments": true,
"include_attachments": false,
"semantic_ranking": {
"weight": 0.7,
"model": "all-MiniLM-L6-v2"
}
}
},
"authentication": {
"type": "none"
}
},
{
"name": "Architecture Docs",
"url": "http://localhost:8000/mcp/context",
"options": {
"max_context_length": 3000,
"filter": {
"space_key": "ARCH",
"labels": ["architecture", "design"],
"include_comments": false,
"include_attachments": true,
"semantic_ranking": {
"weight": 0.8,
"model": "all-MiniLM-L6-v2"
}
}
},
"authentication": {
"type": "none"
}
}
],
"default_endpoint": "API Documentation"
}
```
- Add the path to this file in VS Code settings under "Copilot Chat: MCP Configuration File"
- See `examples/mcp.json` for a full example with multiple endpoints and filtering options
3. **Usage with Copilot:**
- In VS Code, open Copilot Chat (Cmd+I)
- Your queries will now include relevant context from your Confluence pages
- Example: "How do I implement feature X?" will include context from related Confluence documentation
- You can also use `/doc` command in Copilot Chat to explicitly search documentation
4. **Tips for Better Results:**
- Keep Confluence pages well-organized and up-to-date
- Use descriptive titles and labels in Confluence
- Re-crawl after significant documentation updates:
```bash
curl -X POST http://localhost:8000/crawl
```
## Development
1. **Install Development Dependencies:**
```bash
uv pip install -r requirements.txt
```
2. **Using uvx for Development:**
UV installs a command runner called `uvx` that can run Python scripts and modules without explicitly activating the virtual environment:
```bash
# Run the FastAPI server
uvx uvicorn app.main:app --reload
# Run tests
uvx pytest
# Code formatting
uvx black .
uvx isort .
uvx mypy .
```
3. **Environment Configuration:**
The project uses environment variables for configuration. Copy `.env.example` to `.env` and update the values:
```bash
CONFLUENCE_BASE_URL=https://your-domain.atlassian.net
CONFLUENCE_TOKEN=your-api-token
CONFLUENCE_SPACE_KEY=your-space-key
CHROMA_PERSIST_DIR=data/chroma
CHROMA_COLLECTION_NAME=confluence_docs
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
CHUNK_SIZE=512
CHUNK_OVERLAP=50
TOP_K=3
SIMILARITY_THRESHOLD=0.7
```
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes:
- Use `uvx black .` and `uvx isort .` to format code
- Use `uvx mypy .` for type checking
- Add tests for new features
- Update documentation as needed
4. Run tests (`uvx pytest`)
5. Commit your changes (`git commit -m 'Add some amazing feature'`)
6. Push to the branch (`git push origin feature/amazing-feature`)
7. Open a Pull Request
## License
MIT License. See [LICENSE](LICENSE) for more information.
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"description": "# Confluence RAG Data Pipeline with MCP Protocol\n\nA Model Context Protocol (MCP) server that provides relevant context from Confluence pages using RAG (Retrieval Augmented Generation).\n\n[](https://badge.fury.io/py/confluence-scraper-mcp)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n\n## \ud83d\ude80 Quick Start\n\n```bash\n# Install from PyPI\npip install confluence-scraper-mcp\n\n# Set environment variables\nexport CONFLUENCE_BASE_URL=\"https://your-domain.atlassian.net\"\nexport CONFLUENCE_TOKEN=\"your-api-token\"\nexport CONFLUENCE_SPACE_KEY=\"your-space-key\"\n\n# Run as MCP server\nconfluence-scraper-mcp\n\n# Or run as web server\nconfluence-scraper-mcp --web\n```\n\n## Features\n\n- \ud83d\udd0d **Semantic Search**: Uses ChromaDB for vector-based document retrieval\n- \ud83d\udd17 **MCP Integration**: Full Model Context Protocol implementation\n- \ud83d\udcda **Confluence Native**: Direct integration with Confluence API\n- \ud83c\udff7\ufe0f **Smart Filtering**: Filter by spaces, labels, and metadata\n- \ud83d\udcce **Rich Content**: Handles attachments and comments\n- \ud83c\udf10 **Dual Mode**: Run as MCP server or REST API\n- \ud83d\udce6 **Easy Install**: Available on PyPI\n\n## Requirements\n\n- Python 3.9 or higher\n- Confluence API access token\n- ChromaDB for vector storage\n\n## Installation\n\n1. **Install from PyPI (Recommended):**\n ```bash\n pip install confluence-scraper-mcp\n ```\n\n2. **Install UV if you haven't already:**\n ```bash\n curl -LsSf https://astral.sh/uv/install.sh | sh\n ```\n\n3. **Clone and Setup Project (Development):**\n ```bash\n git clone <repository-url>\n cd confluence-scraper-mcp\n # Create virtual environment\n uv venv .venv\n # Activate virtual environment\n source .venv/bin/activate\n # Install dependencies\n uv pip install -r requirements.txt\n ```\n\n4. **Configure Environment:**\n - Create a `.env` file in the project root:\n ```bash\n touch .env\n ```\n - Add the following configuration (adjust values as needed):\n ```bash\n # Required settings\n CONFLUENCE_BASE_URL=https://your-domain.atlassian.net\n CONFLUENCE_TOKEN=your-api-token\n CONFLUENCE_SPACE_KEY=optional-space-key\n \n # Optional settings (with defaults)\n INITIAL_CRAWL=false\n CHROMA_PERSIST_DIR=./data/chroma\n EMBEDDING_MODEL=\"all-MiniLM-L6-v2\"\n MAX_PAGES=1000\n INCLUDE_ATTACHMENTS=true\n INCLUDE_COMMENTS=true\n ```\n\n## Usage\n\n### Command Line Interface (After PyPI Installation)\n\n```bash\n# Run as MCP server (stdio mode) - default\nconfluence-scraper-mcp\n\n# Run as web server\nconfluence-scraper-mcp --web\n```\n\n### Development Mode\n\n1. **Using uvx (Recommended):**\n ```bash\n # Development mode with auto-reload\n uvx uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload\n \n # Run tests\n uvx pytest\n \n # Code formatting and checks\n uvx black .\n uvx isort .\n uvx mypy .\n ```\n\n2. **Alternative: Using Virtual Environment:**\n ```bash\n # Activate virtual environment\n source .venv/bin/activate\n \n # Then run commands as usual\n uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload\n ```\n\n3. **Initial Setup:**\n ```bash\n # Start initial crawl of Confluence pages\n curl -X POST http://localhost:8000/crawl\n \n # Verify server health\n curl http://localhost:8000/health\n ```\n\n4. **Use the MCP API:**\n ```bash\n # Get context for an LLM query\n curl -X POST http://localhost:8000/mcp/context \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"messages\": [{\"role\": \"user\", \"content\": \"Tell me about project X\"}],\n \"query\": \"project X documentation\",\n \"max_context_length\": 1000\n }'\n \n # The response will include relevant context from your Confluence pages\n ```\n\n5. **Monitor and Maintain:**\n ```bash\n # View logs\n tail -f logs/app.log\n \n # Re-crawl Confluence (e.g., after updates)\n curl -X POST http://localhost:8000/crawl\n ```\n\n## API Endpoints\n\n- `GET /health`: Health check endpoint\n- `POST /crawl`: Trigger Confluence crawl\n- `POST /mcp/context`: Get relevant context for a query\n\n## MCP (Model Context Protocol) Configuration\n\nThis server implements the Model Context Protocol (MCP) for seamless integration with AI assistants and LLM clients. \n\n### Quick MCP Setup\n\n1. **Install the package:**\n ```bash\n pip install confluence-scraper-mcp\n ```\n\n2. **Copy the MCP configuration:**\n ```bash\n # Copy the example configuration\n cp examples/mcp-client-config.json ~/.config/your-mcp-client/\n ```\n\n3. **Update environment variables in the config:**\n ```json\n {\n \"mcpServers\": {\n \"confluence-scraper-mcp\": {\n \"command\": \"confluence-scraper-mcp\",\n \"args\": [],\n \"env\": {\n \"CONFLUENCE_BASE_URL\": \"https://your-domain.atlassian.net\",\n \"CONFLUENCE_TOKEN\": \"your-api-token\",\n \"CONFLUENCE_SPACE_KEY\": \"your-space-key\"\n }\n }\n }\n }\n ```\n\n### MCP Tools Available\n\nThe server provides several MCP tools:\n\n- **`confluence_search`**: Search Confluence pages using semantic search\n- **`confluence_get_page`**: Retrieve specific page content by ID or title\n- **`confluence_crawl`**: Trigger crawling and indexing of content\n\n### Example MCP Tool Usage\n\n```json\n{\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"confluence_search\",\n \"arguments\": {\n \"query\": \"API authentication methods\",\n \"space_key\": \"DEV\",\n \"max_results\": 3,\n \"include_attachments\": true\n }\n }\n}\n```\n\n### MCP Configuration Files\n\nThe package includes example configuration files:\n\n- **`examples/mcp.json`**: Complete MCP server specification\n- **`examples/mcp-client-config.json`**: Simple client configuration\n\nSee the [MCP specification](https://spec.modelcontextprotocol.io/) for more details on the protocol.\n\n## \ud83e\udd16 GitHub Copilot Integration\n\n### Quick Setup for Copilot\n\n1. **Install the package:**\n ```bash\n pip install confluence-scraper-mcp\n ```\n\n2. **Configure VS Code Settings:**\n Open VS Code settings (`Cmd+,`) and add to your `settings.json`:\n ```json\n {\n \"github.copilot.chat.mcpServers\": {\n \"confluence-rag\": {\n \"command\": \"confluence-scraper-mcp\",\n \"args\": [],\n \"env\": {\n \"CONFLUENCE_BASE_URL\": \"https://your-domain.atlassian.net\",\n \"CONFLUENCE_TOKEN\": \"your-api-token\",\n \"CONFLUENCE_SPACE_KEY\": \"your-space-key\"\n }\n }\n }\n }\n ```\n\n3. **Initial Setup:**\n ```bash\n # Start server and crawl content\n confluence-scraper-mcp --web &\n curl -X POST http://localhost:8000/crawl\n ```\n\n4. **Test with Copilot:**\n Open Copilot Chat and ask: *\"How do we handle authentication in our system?\"*\n\n### Detailed Setup Guide\n\nFor complete setup instructions, see: **[\ud83d\udcd6 Copilot Setup Guide](docs/COPILOT_SETUP.md)**\n\n## Using with Code Assistants\n\nThis MCP server specializes in Confluence documentation and uses RAG (Retrieval Augmented Generation) with ChromaDB:\n\n**Key Features:**\n- \ud83d\udd17 **Confluence Integration**: Direct API integration with page, attachment, and comment handling\n- \ud83d\udd0d **Semantic Search**: ChromaDB vector search for meaning-based retrieval\n- \ud83c\udff7\ufe0f **Smart Filtering**: Filter by space keys, labels, content types\n- \ud83d\udcca **Metadata Preservation**: Maintains Confluence structure and relationships\n ```json\n {\n \"endpoints\": [\n {\n \"name\": \"API Documentation\",\n \"url\": \"http://localhost:8000/mcp/context\",\n \"options\": {\n \"max_context_length\": 2000,\n \"filter\": {\n \"space_key\": \"API\",\n \"labels\": [\"technical-docs\", \"api-reference\"],\n \"include_comments\": true,\n \"include_attachments\": false,\n \"semantic_ranking\": {\n \"weight\": 0.7,\n \"model\": \"all-MiniLM-L6-v2\"\n }\n }\n },\n \"authentication\": {\n \"type\": \"none\"\n }\n },\n {\n \"name\": \"Architecture Docs\",\n \"url\": \"http://localhost:8000/mcp/context\",\n \"options\": {\n \"max_context_length\": 3000,\n \"filter\": {\n \"space_key\": \"ARCH\",\n \"labels\": [\"architecture\", \"design\"],\n \"include_comments\": false,\n \"include_attachments\": true,\n \"semantic_ranking\": {\n \"weight\": 0.8,\n \"model\": \"all-MiniLM-L6-v2\"\n }\n }\n },\n \"authentication\": {\n \"type\": \"none\"\n }\n }\n ],\n \"default_endpoint\": \"API Documentation\"\n }\n ```\n - Add the path to this file in VS Code settings under \"Copilot Chat: MCP Configuration File\"\n - See `examples/mcp.json` for a full example with multiple endpoints and filtering options\n\n3. **Usage with Copilot:**\n - In VS Code, open Copilot Chat (Cmd+I)\n - Your queries will now include relevant context from your Confluence pages\n - Example: \"How do I implement feature X?\" will include context from related Confluence documentation\n - You can also use `/doc` command in Copilot Chat to explicitly search documentation\n\n4. **Tips for Better Results:**\n - Keep Confluence pages well-organized and up-to-date\n - Use descriptive titles and labels in Confluence\n - Re-crawl after significant documentation updates:\n ```bash\n curl -X POST http://localhost:8000/crawl\n ```\n\n## Development\n\n1. **Install Development Dependencies:**\n ```bash\n uv pip install -r requirements.txt\n ```\n\n2. **Using uvx for Development:**\n UV installs a command runner called `uvx` that can run Python scripts and modules without explicitly activating the virtual environment:\n ```bash\n # Run the FastAPI server\n uvx uvicorn app.main:app --reload\n \n # Run tests\n uvx pytest\n \n # Code formatting\n uvx black .\n uvx isort .\n uvx mypy .\n ```\n\n3. **Environment Configuration:**\n The project uses environment variables for configuration. Copy `.env.example` to `.env` and update the values:\n ```bash\n CONFLUENCE_BASE_URL=https://your-domain.atlassian.net\n CONFLUENCE_TOKEN=your-api-token\n CONFLUENCE_SPACE_KEY=your-space-key\n CHROMA_PERSIST_DIR=data/chroma\n CHROMA_COLLECTION_NAME=confluence_docs\n EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2\n CHUNK_SIZE=512\n CHUNK_OVERLAP=50\n TOP_K=3\n SIMILARITY_THRESHOLD=0.7\n ```\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Make your changes:\n - Use `uvx black .` and `uvx isort .` to format code\n - Use `uvx mypy .` for type checking\n - Add tests for new features\n - Update documentation as needed\n4. Run tests (`uvx pytest`)\n5. Commit your changes (`git commit -m 'Add some amazing feature'`)\n6. Push to the branch (`git push origin feature/amazing-feature`)\n7. Open a Pull Request\n\n## License\n\nMIT License. See [LICENSE](LICENSE) for more information.\n",
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