WebSearch is a self-hosted MCP server that enables AI assistants to perform real-time web searches and retrieve current information from the internet. It connects AI models to a companion Crawler API service that handles the actual search queries. Key capabilities of WebSearch: - Real-time web search integration for AI assistants via MCP protocol - Self-hosted architecture — run the crawler API on local machines or VPS for full control and privacy - Configurable search parameters including maximum result limits - Results include titles, URLs, and content snippets from web pages - Compatible with Claude Desktop, Cursor IDE, Cline, and other MCP-supporting clients

WebSearch-MCP

[](https://smithery.ai/server/@mnhlt/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

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.

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:

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

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:

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 endpoin