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A Model Context Protocol (MCP) server based on multi-engine search results, supporting free web search without API keys.

• Web search using multi-engine results
  • bing
  • baidu
  • linux.do temporarily unsupported
  • csdn
  • duckduckgo
  • exa
  • brave
• HTTP proxy configuration support for accessing restricted resources
• No API keys or authentication required
• Returns structured results with titles, URLs, and descriptions
• Configurable number of results per search
• Customizable default search engine
• Support for fetching individual article content
  • csdn

• Support for Bing (already supported), DuckDuckGo (already supported), Exa (already supported), Brave (already supported), Google and other search engines
• Support for more blogs, forums, and social platforms
• Optimize article content extraction, add support for more sites

1. Clone or download this repository
2. Install dependencies:

npm install

3. Build the server:

npm run build

4. Add the server to your MCP configuration:

Cherry Studio:

{
  "mcpServers": {
    "web-search": {
      "name": "Web Search MCP",
      "type": "streamableHttp",
      "description": "Multi-engine web search with article fetching",
      "isActive": true,
      "baseUrl": "https://localhost:3000/mcp"
    }
  }
}

VSCode (Claude Dev Extension):

{
  "mcpServers": {
    "web-search": {
      "transport": {
        "type": "streamableHttp",
        "url": "https://localhost:3000/mcp"
      }
    },
    "web-search-sse": {
      "transport": {
        "type": "sse",
        "url": "https://localhost:3000/sse"
      }
    }
  }
}

Claude Desktop:

{
  "mcpServers": {
    "web-search": {
      "transport": {
        "type": "streamableHttp",
        "url": "https://localhost:3000/mcp"
      }
    },
    "web-search-sse": {
      "transport": {
        "type": "sse",
        "url": "https://localhost:3000/sse"
      }
    }
  }
}

Quick deployment using Docker Compose:

docker-compose up -d

Or use Docker directly:

docker run -d --name web-search -p 3000:3000 -e ENABLE_CORS=true -e CORS_ORIGIN=* ghcr.io/aas-ee/open-web-search:latest

Environment variable configuration:

# Enable CORS (default: false)
ENABLE_CORS=true
# CORS origin configuration (default: *)
CORS_ORIGIN=*
# Default search engine (options: bing, duckduckgo, exa, brave, default: bing)
DEFAULT_SEARCH_ENGINE=duckduckgo
# Enable HTTP proxy (default: false)
USE_PROXY=true
# Proxy server URL (default: https://127.0.0.1:10809)
PROXY_URL=https://your-proxy-server:port
# Server port (default: 3000)
PORT=8080

Then configure in your MCP client:

{
  "mcpServers": {
    "web-search": {
      "name": "Web Search MCP",
      "type": "streamableHttp",
      "description": "Multi-engine web search with article fetching",
      "isActive": true,
      "baseUrl": "https://localhost:3000/mcp"
    },
    "web-search-sse": {
      "transport": {
        "name": "Web Search MCP",
        "type": "sse",
        "description": "Multi-engine web search with article fetching",
        "isActive": true,
        "url": "https://localhost:3000/sse"
      }
    }
  }
}

The server provides three tools: search, fetchLinuxDoArticle, and fetchCsdnArticle.

{
  "query": string,        // Search query
  "limit": number,        // Optional: Number of results to return (default: 10)
  "engines": string[]     // Optional: Engines to use (bing,baidu,linuxdo,csdn,duckduckgo,exa,brave) default bing
}

Usage example:

use_mcp_tool({
  server_name: "web-search",
  tool_name: "search",
  arguments: {
    query: "search content",
    limit: 3,  // Optional parameter
    engines: ["bing", "csdn", "duckduckgo", "exa", "brave"] // Optional parameter, supports multi-engine combined search
  }
})

Response example:

[
  {
    "title": "Example Search Result",
    "url": "https://example.com",
    "description": "Description text of the search result...",
    "source": "Source",
    "engine": "Engine used"
  }
]

Used to fetch complete content of CSDN blog articles.

{
  "url": string    // URL from CSDN search results using the search tool
}

Usage example:

use_mcp_tool({
  server_name: "web-search",
  tool_name: "fetchCsdnArticle",
  arguments: {
    url: "https://blog.csdn.net/xxx/article/details/xxx"
  }
})

Response example:

[
  {
    "content": "Example search result"
  }
]

Used to fetch complete content of Linux.do forum articles.

{
  "url": string    // URL from linuxdo search results using the search tool
}

Usage example:

use_mcp_tool({
  server_name: "web-search",
  tool_name: "fetchLinuxDoArticle",
  arguments: {
    url: "https://xxxx.json"
  }
})

Response example:

[
  {
    "content": "Example search result"
  }
]

Since this tool works by scraping multi-engine search results, please note the following important limitations:

1. Rate Limiting:

   • Too many searches in a short time may cause the used engines to temporarily block requests
   • Recommendations:
     • Maintain reasonable search frequency
     • Use the limit parameter judiciously
     • Add delays between searches when necessary

2. Result Accuracy:

   • Depends on the HTML structure of corresponding engines, may fail when engines update
   • Some results may lack metadata like descriptions
   • Complex search operators may not work as expected

3. Legal Terms:

   • This tool is for personal use only
   • Please comply with the terms of service of corresponding engines
   • Implement appropriate rate limiting based on your actual use case

4. Search Engine Configuration:

   • Default search engine can be set via the DEFAULT_SEARCH_ENGINE environment variable
   • Supported engines: bing, duckduckgo, exa, brave
   • The default engine is used when searching specific websites

5. Proxy Configuration:

   • HTTP proxy can be configured when certain search engines are unavailable in specific regions
   • Enable proxy with environment variable USE_PROXY=true
   • Configure proxy server address with PROXY_URL

Welcome to submit issue reports and feature improvement suggestions!

If you want to fork this repository and publish your own Docker image, you need to make the following configurations:

To enable automatic Docker image building and publishing, please add the following secrets in your GitHub repository settings (Settings → Secrets and variables → Actions):

Required Secrets:

• GITHUB_TOKEN: Automatically provided by GitHub (no setup needed)

Optional Secrets (for Alibaba Cloud ACR):

• ACR_REGISTRY: Your Alibaba Cloud Container Registry URL (e.g., registry.cn-hangzhou.aliyuncs.com)
• ACR_USERNAME: Your Alibaba Cloud ACR username
• ACR_PASSWORD: Your Alibaba Cloud ACR password
• ACR_IMAGE_NAME: Your image name in ACR (e.g., your-namespace/open-web-search)

The repository includes a GitHub Actions workflow (.github/workflows/docker.yml) that automatically:

1. Trigger Conditions:

   • Push to main branch
   • Push version tags (v*)
   • Manual workflow trigger

2. Build and Push to:

   • GitHub Container Registry (ghcr.io) - always enabled
   • Alibaba Cloud Container Registry - only enabled when ACR secrets are configured

3. Image Tags:

   • ghcr.io/your-username/open-web-search:latest
   • your-acr-address/your-image-name:latest (if ACR is configured)

1. Fork the repository to your GitHub account
2. Configure secrets (if you need ACR publishing):
   • Go to Settings → Secrets and variables → Actions in your forked repository
   • Add the ACR-related secrets listed above
3. Push changes to the main branch or create version tags
4. GitHub Actions will automatically build and push your Docker image
5. Use your image, update the Docker command:

   docker run -d --name web-search -p 3000:3000 -e ENABLE_CORS=true -e CORS_ORIGIN=* ghcr.io/your-username/open-web-search:latest

• If you don't configure ACR secrets, the workflow will only publish to GitHub Container Registry
• Make sure your GitHub repository has Actions enabled
• The workflow will use your GitHub username (converted to lowercase) as the GHCR image name

If you find this project helpful, please consider giving it a ⭐ Star!