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

pip install -r requirements.txt

Using Docker

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:

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:

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:

# TRANSPORT = "streamable-http"
TRANSPORT = "stdio"

And update the run call:

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:

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

# 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

bing-search-mcp