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Adityas7netapp

Bing Search MCP Server

by Adityas7netapp
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Python
Remote

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

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