Bing Search MCP Server

What does “Bing Search” MCP Server do?

The Bing Search MCP Server is a Model Context Protocol (MCP) server that integrates the Microsoft Bing Search API, enabling AI assistants to perform a variety of search tasks. By serving as a bridge between AI clients and Bing’s powerful search capabilities, it facilitates web, news, and image searches. This empowers AI-driven development workflows with up-to-date information, visual content retrieval, and news aggregation. The server also features rate limiting to prevent API misuse and comprehensive error handling to ensure robust interactions. It is designed to be compatible with multiple clients, such as Claude Desktop and Cursor, making it a versatile tool for enhancing the breadth and relevance of AI assistant outputs.

List of Prompts

No explicit prompt templates are mentioned in the repository.

List of Resources

No explicit resources are documented in the repository.

List of Tools

• bing_web_search
  General web search for information, websites, and content.
  bing_web_search(query: str, count: int = 10, offset: int = 0, market: str = "en-US")
• bing_news_search
  Listed as available but no further details provided in the visible content.

Note: Image search is mentioned as a feature, but no tool details are visible for it in the available documentation.

Use Cases of this MCP Server

• Web Information Retrieval
  Developers and AI assistants can retrieve up-to-date, relevant information from the web to inform decision-making or provide users with rich, contextual responses.
• News Aggregation
  Quickly gather the latest news articles and updates on specific topics, making it easy for AI to provide timely information or summaries.
• Image Content Retrieval
  Access and present visual content from Bing’s image search, useful for tasks requiring illustration, design, or multimedia content.
• Research Automation
  Streamline the process of collecting information from multiple sources, saving time in research-heavy workflows.
• Content Curation
  Aggregate and filter web or news content for newsletters, reports, or automated content pipelines.

How to set it up

Windsurf

1. Ensure Python 3.10+ and uv are installed.
2. Clone the repository and install dependencies.
3. Set your Bing API key in your environment variables.
4. In your Windsurf configuration, add the MCP server entry with the appropriate command.
5. Save the configuration and restart Windsurf.

JSON Example:

{
  "mcpServers": {
    "bing-search": {
      "command": "uvx",
      "args": ["/path/to/your/bing-search-mcp"],
      "env": {
        "BING_API_KEY": "your-bing-api-key"
      }
    }
  }
}

Claude

1. Ensure Python 3.10+ and uv are installed.
2. Clone the repository and install dependencies.
3. Add your Bing API key to your environment variables.
4. Edit claude_desktop_config.json to include the MCP server.
5. Save and restart Claude Desktop.

JSON Example:

{
  "mcpServers": {
    "bing-search": {
      "command": "uvx",
      "args": ["/path/to/your/bing-search-mcp"],
      "env": {
        "BING_API_KEY": "your-bing-api-key"
      }
    }
  }
}

Cursor

1. Install Python 3.10+ and uv.
2. Clone and set up the repository.
3. Configure environment variables for the Bing API key.
4. Edit Cursor’s configuration to add the MCP server.
5. Restart Cursor to activate the integration.

JSON Example:

{
  "mcpServers": {
    "bing-search": {
      "command": "uvx",
      "args": ["/path/to/your/bing-search-mcp"],
      "env": {
        "BING_API_KEY": "your-bing-api-key"
      }
    }
  }
}

Cline

1. Make sure Python 3.10+ and uv are installed.
2. Clone the repository and install dependencies as per instructions.
3. Set the Bing API key as an environment variable.
4. Update Cline’s MCP configuration with the server details.
5. Restart Cline to apply changes.

JSON Example:

{
  "mcpServers": {
    "bing-search": {
      "command": "uvx",
      "args": ["/path/to/your/bing-search-mcp"],
      "env": {
        "BING_API_KEY": "your-bing-api-key"
      }
    }
  }
}

Securing API Keys:
Always use environment variables to store sensitive keys. Place your key in the env field of the server configuration, as shown above. Never hard-code keys directly in source files.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "bing-search": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change "bing-search" to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The Bing Search MCP Server provides a focused integration with Bing’s search APIs, making it a valuable resource for AI assistants needing real-time information and content. However, the documentation lacks depth in areas such as prompt templates and explicit resource definitions. The tool’s security practices and cross-platform support are strong. Overall, it is robust for its primary function but could be improved with more comprehensive MCP feature support.

MCP Score