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