# Confluence MCP Server

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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.