# Bing Search MCP Server
A Model Context Protocol (MCP) server that provides AI-grounded Bing search capabilities using Azure AI Project Client. This server enables intelligent web searches with citation tracking and URL extraction.
## Features
- **AI-Grounded Search**: Leverages Azure AI agents for intelligent search results
- **Citation Tracking**: Automatically extracts and formats citations with URLs
- **MCP Protocol**: Compatible with MCP clients for seamless integration
- **HTTP Transport**: Runs as HTTP server on port 8000 for remote access
## Prerequisites
- Python 3.8 or higher
- Azure subscription with AI Project configured
- Azure AD App Registration (for service principal authentication)
- Bing Search resource enabled in your Azure AI Project
## Installation
### Using pip
```bash
pip install -r requirements.txt
```
### Using Docker
```bash
docker build -t bing-search-mcp .
docker run --env-file .env bing-search-mcp
```
## Configuration
Create a `.env` file in the project root with the following variables:
```env
PROJECT_ENDPOINT=https://your-project.cognitiveservices.azure.com/
AGENT_ID=your-agent-id
TENANT_ID=your-azure-tenant-id
CLIENT_ID=your-azure-client-id
CLIENT_SECRET=your-azure-client-secret
```
See `.env.example` for a template.
## Usage
### Running the Server
#### HTTP Mode (default)
The server runs on `http://0.0.0.0:8000` by default:
```bash
python bing-search.py
```
The server will be accessible at:
- Local: `http://localhost:8000`
- Network: `http://<your-ip>:8000`
#### Stdio Mode
To use stdio transport, modify `bing-search.py`:
```python
# TRANSPORT = "streamable-http"
TRANSPORT = "stdio"
```
And update the run call:
```python
bing_mcp.run(transport=TRANSPORT)
```
### Available Tools
#### `bing_grounded_with_ai`
Performs an AI-grounded Bing search with citation tracking.
**Parameters:**
- `query` (str): The search query
**Returns:**
- Response text with inline citations in Markdown format
- List of extracted URLs with titles
**Example:**
```python
result_text, urls = bing_grounded_with_ai("What is the latest news about AI?")
print(result_text) # Text with [Title](URL) citations
print(urls) # [["Title1", "url1"], ["Title2", "url2"], ...]
```
## Architecture
The server uses:
- **FastMCP**: Simplified MCP server implementation
- **Azure AI Project Client**: Manages AI agents and threads
- **Azure Identity**: Handles service principal authentication
## Error Handling
The server includes comprehensive error handling for:
- Missing or invalid queries
- Azure credential issues
- Agent execution failures
- Thread cleanup errors
All errors are returned as `ToolError` exceptions with descriptive messages.
## Development
### Project Structure
```
bing-search/
├── bing-search.py # Main MCP server implementation
├── requirements.txt # Python dependencies
├── Dockerfile # Container configuration
├── .env # Environment variables (not in git)
├── .env.example # Environment template
└── README.md # This file
```
### Transport Modes
- **streamable-http**: HTTP server for remote access (default, port 8000)
- **stdio**: Communicates via standard input/output (alternative mode)
## Docker Support
The included Dockerfile creates a lightweight container optimized for production use:
- Based on Python 3.11-slim for minimal image size
- Non-root user for enhanced security
- Multi-stage build process
- Optimized layer caching
### Build and Run
```bash
# Build the image
docker build -t bing-search-mcp .
# Run with environment file
docker run -p 8000:8000 --env-file .env bing-search-mcp
# Run with individual environment variables
docker run -p 8000:8000 \
-e PROJECT_ENDPOINT="your-endpoint" \
-e AGENT_ID="your-agent-id" \
-e TENANT_ID="your-tenant-id" \
-e CLIENT_ID="your-client-id" \
-e CLIENT_SECRET="your-client-secret" \
bing-search-mcp
# Access the server
curl http://localhost:8000
```
## Security Considerations
- Never commit `.env` file or credentials to version control
- Use Azure Key Vault for production secrets management
- Rotate service principal credentials regularly
- Apply principle of least privilege to Azure AD app permissions
## Troubleshooting
### Common Issues
**Authentication Errors:**
- Verify all Azure credentials are correct
- Ensure service principal has appropriate permissions
- Check if credentials have expired
**Agent Not Found:**
- Confirm AGENT_ID exists in your Azure AI Project
- Verify PROJECT_ENDPOINT is correct
**Connection Issues:**
- Check network connectivity to Azure
- Verify firewall rules allow outbound HTTPS
## License
MIT
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## Support
For issues and questions:
- Create an issue in the repository
- Check Azure AI Project documentation
- Review MCP protocol specifications
