WebSearch-MCP

A Model Context Protocol (MCP) server implementation that provides a web search capability over stdio transport. This server integrates with a WebSearch Crawler API to retrieve search results.

Table of Contents

• About
• Installation
• Configuration
• Setup & Integration
  • Setting Up the Crawler Service
    • Prerequisites
    • Starting the Crawler Service
    • Testing the Crawler API
    • Custom Configuration
  • Integrating with MCP Clients
    • Quick Reference: MCP Configuration
    • Claude Desktop
    • Cursor IDE
    • Cline
• Usage
  • Parameters
  • Example Search Response
  • Testing Locally
  • As a Library
• Troubleshooting
  • Crawler Service Issues
  • MCP Server Issues
• Development
  • Project Structure
  • Publishing to npm
• Contributing
• License

About

WebSearch-MCP is a Model Context Protocol server that provides web search capabilities to AI assistants that support MCP. It allows AI models like Claude to search the web in real-time, retrieving up-to-date information about any topic.

The server integrates with a Crawler API service that handles the actual web searches, and communicates with AI assistants using the standardized Model Context Protocol.

Integrating with MCP Clients

Quick Reference: MCP Configuration

Here's a quick reference for MCP configuration across different clients:

{
    "mcpServers": {
        "websearch": {
            "command": "npx",
            "args": [
                "websearch-mcp"
            ],
            "environment": {
                "API_URL": "http://localhost:3001",
                "MAX_SEARCH_RESULT": "5" // reduce to save your tokens, increase for wider information gain
            }
        }
    }
}

Workaround for Windows, due to Issue

{
	"mcpServers": {
	  "websearch": {
            "command": "cmd",
            "args": [
				"/c",
				"npx",
                "websearch-mcp"
            ],
            "environment": {
                "API_URL": "http://localhost:3001",
                "MAX_SEARCH_RESULT": "1"
            }
        }
	}
  }

Development

To work on this project:

1. Clone the repository
2. Install dependencies: npm install
3. Build the project: npm run build
4. Run in development mode: npm run dev

The server expects a WebSearch Crawler API as defined in the included swagger.json file. Make sure the API is running at the configured API_URL.

Project Structure

• .gitignore: Specifies files that Git should ignore (node_modules, dist, logs, etc.)
• .npmignore: Specifies files that shouldn't be included when publishing to npm
• package.json: Project metadata and dependencies
• src/: Source TypeScript files
• dist/: Compiled JavaScript files (generated when building)

Publishing to npm

To publish this package to npm:

1. Make sure you have an npm account and are logged in (npm login)
2. Update the version in package.json (npm version patch|minor|major)
3. Run npm publish

The .npmignore file ensures that only the necessary files are included in the published package:

• The compiled code in dist/
• README.md and LICENSE files
• package.json

Installation

Installing via Smithery

To install WebSearch for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @mnhlt/WebSearch-MCP --client claude

Manual Installation

npm install -g websearch-mcp

Or use without installing:

npx websearch-mcp

Configuration

The WebSearch MCP server can be configured using environment variables:

• API_URL: The URL of the WebSearch Crawler API (default: http://localhost:3001)
• MAX_SEARCH_RESULT: Maximum number of search results to return when not specified in the request (default: 5)

Examples:

# Configure API URL
API_URL=https://crawler.example.com npx websearch-mcp

# Configure maximum search results
MAX_SEARCH_RESULT=10 npx websearch-mcp

# Configure both
API_URL=https://crawler.example.com MAX_SEARCH_RESULT=10 npx websearch-mcp

Setup & Integration

Setting up WebSearch-MCP involves two main parts: configuring the crawler service that performs the actual web searches, and integrating the MCP server with your AI client applications.

Setting Up the Crawler Service

The WebSearch MCP server requires a crawler service to perform the actual web searches. You can easily set up the crawler service using Docker Compose.

Prerequisites

• Docker and Docker Compose

Starting the Crawler Service

1. Create a file named docker-compose.yml with the following content:

version: '3.8'

services:
  crawler:
    image: laituanmanh/websearch-crawler:latest
    container_name: websearch-api
    restart: unless-stopped
    ports:
      - "3001:3001"
    environment:
      - NODE_ENV=production
      - PORT=3001
      - LOG_LEVEL=info
      - FLARESOLVERR_URL=http://flaresolverr:8191/v1
    depends_on:
      - flaresolverr
    volumes:
      - crawler_storage:/app/storage

  flaresolverr:
    image: 21hsmw/flaresolverr:nodriver
    container_name: flaresolverr
    restart: unless-stopped
    environment:
      - LOG_LEVEL=info
      - TZ=UTC

volumes:
  crawler_storage:

workaround for Mac Apple Silicon

version: '3.8'

services:
  crawler:
    image: laituanmanh/websearch-crawler:latest
    container_name: websearch-api
    platform: "linux/amd64"
    restart: unless-stopped
    ports:
      - "3001:3001"
    environment:
      - NODE_ENV=production
      - PORT=3001
      - LOG_LEVEL=info
      - FLARESOLVERR_URL=http://flaresolverr:8191/v1
    depends_on:
      - flaresolverr
    volumes:
      - crawler_storage:/app/storage

  flaresolverr:
    image: 21hsmw/flaresolverr:nodriver
    platform: "linux/arm64"
    container_name: flaresolverr
    restart: unless-stopped
    environment:
      - LOG_LEVEL=info
      - TZ=UTC

volumes:
  crawler_storage:

2. Start the services:

docker-compose up -d

3. Verify that the services are running:

docker-compose ps

4. Test the crawler API health endpoint:

curl http://localhost:3001/health

Expected response:

{
  "status": "ok",
  "details": {
    "status": "ok",
    "flaresolverr": true,
    "google": true,
    "message": null
  }
}

The crawler API will be available at http://localhost:3001.

Testing the Crawler API

You can test the crawler API directly using curl:

curl -X POST http://localhost:3001/crawl \
  -H "Content-Type: application/json" \
  -d '{
    "query": "typescript best practices",
    "numResults": 2,
    "language": "en",
    "filters": {
      "excludeDomains": ["youtube.com"],
      "resultType": "all" 
    }
  }'

Custom Configuration

You can customize the crawler service by modifying the environment variables in the docker-compose.yml file:

• PORT: The port on which the crawler API listens (default: 3001)
• LOG_LEVEL: Logging level (options: debug, info, warn, error)
• FLARESOLVERR_URL: URL of the FlareSolverr service (for bypassing Cloudflare protection)

Usage

This package implements an MCP server using stdio transport that exposes a web_search tool with the following parameters:

Parameters

• query (required): The search query to look up
• numResults (optional): Number of results to return (default: 5)
• language (optional): Language code for search results (e.g., 'en')
• region (optional): Region code for search results (e.g., 'us')
• excludeDomains (optional): Domains to exclude from results
• includeDomains (optional): Only include these domains in results
• excludeTerms (optional): Terms to exclude from results
• resultType (optional): Type of results to return ('all', 'news', or 'blogs')

Example Search Response

Here's an example of a search response:

{
  "query": "machine learning trends",
  "results": [
    {
      "title": "Top Machine Learning Trends in 2025",
      "snippet": "The key machine learning trends for 2025 include multimodal AI, generative models, and quantum machine learning applications in enterprise...",
      "url": "https://example.com/machine-learning-trends-2025",
      "siteName": "AI Research Today",
      "byline": "Dr. Jane Smith"
    },
    {
      "title": "The Evolution of Machine Learning: 2020-2025",
      "snippet": "Over the past five years, machine learning has evolved from primarily supervised learning approaches to more sophisticated self-supervised and reinforcement learning paradigms...",
      "url": "https://example.com/ml-evolution",
      "siteName": "Tech Insights",
      "byline": "John Doe"
    }
  ]
}

Testing Locally

To test the WebSearch MCP server locally, you can use the included test client:

npm run test-client

This will start the MCP server and a simple command-line interface that allows you to enter search queries and see the results.

You can also configure the API_URL for the test client:

API_URL=https://crawler.example.com npm run test-client

As a Library

You can use this package programmatically:

import { createMCPClient } from '@modelcontextprotocol/sdk';

// Create an MCP client
const client = createMCPClient({
  transport: { type: 'subprocess', command: 'npx websearch-mcp' }
});

// Execute a web search
const response = await client.request({
  method: 'call_tool',
  params: {
    name: 'web_search',
    arguments: {
      query: 'your search query',
      numResults: 5,
      language: 'en'
    }
  }
});

console.log(response.result);

Troubleshooting

Crawler Service Issues

• API Unreachable: Ensure that the crawler service is running and accessible at the configured API_URL.
• Search Results Not Available: Check the logs of the crawler service to see if there are any errors:
  Copy

  docker-compose logs crawler

• FlareSolverr Issues: Some websites use Cloudflare protection. If you see errors related to this, check if FlareSolverr is working:
  Copy

  docker-compose logs flaresolverr

MCP Server Issues

• Import Errors: Ensure you have the latest version of the MCP SDK:
  Copy

  npm install -g @modelcontextprotocol/sdk@latest

• Connection Issues: Make sure the stdio transport is properly configured for your client.

License

ISC