web-search-mcp

web-search-mcp is a local-AI tool - A simple, locally hosted Web Search MCP server for use with Local LLMs.

Overview

A TypeScript MCP (Model Context Protocol) server that provides comprehensive web search capabilities using direct connections (no API keys required) with multiple tools for different use cases.

When a comprehensive search is requested, the server uses an optimised search strategy: 1. Browser-based Bing Search - Primary method using dedicated Chromium instance 2. Browser-based Brave Search - Secondary option using dedicated Firefox instance 3. Axios DuckDuckGo Search - Final fallback using traditional HTTP 4. Dedicated browser isolation: Each search engine gets its own browser instance with automatic cleanup 5. Content extraction: Tries axios first, then falls back to browser with human behavior simulation 6. Concurrent processing: Extracts content from multiple pages simultaneously with timeout protection 7. HTTP/2 error recovery: Automatically falls back to HTTP/1.1 when protocol errors occur

For quick search results without full content extraction: 1. Performs the same optimised multi-engine search as 'full-web-search' 2. Returns only the search result snippets/descriptions 3. Does not follow links to extract full page content

For extracting content from a specific webpage: 1. Takes a single URL as input 2. Follows the URL and extracts the main page content 3. Removes navigation, ads, and other non-content elements

This MCP server has been developed and tested with LM Studio and LibreChat. It has not been tested with other MCP clients.

web-search-mcp is open-source, written primarily in TypeScript, with 929 GitHub stars under the MIT license. The latest release is v0.3.2 (2025-08-07).

Key capabilities

From the project's documentation:

• Multi-Engine Web Search: Prioritises Bing > Brave > DuckDuckGo for optimal reliability and performance
• Full Page Content Extraction: Fetches and extracts complete page content from search results
• Multiple Search Tools: Three specialised tools for different use cases
• Smart Request Strategy: Switches between playwright browesrs and fast axios requests to ensure results are returned
• Concurrent Processing: Extracts content from multiple pages simultaneously
• ✅ Works well with: Qwen3

How it fits a local-AI stack

web-search-mcp runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related local-AI tools in the directory:

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Sources

• Source code & docs: mrkrsl/web-search-mcp

Stats from GitHub, 2026-06-08.