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πŸš€ OneSearch MCP Server: Web Search & Scraper & Extract, Support agent-browser, SearXNG, Tavily, DuckDuckGo, Bing, etc.

Publisheryokingma
Repositoryone-search-mcp
LanguageTypeScript
Forks
18
Stars
114
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    One Search exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    114 stars and 18 forks from the linked repository.

πŸš€ OneSearch MCP Server: Web Search & Crawl & Scraper & Content Prep

A Model Context Protocol (MCP) server implementation that integrates with multiple search providers for web search, local browser search, and scraping capabilities with agent-browser.

Features

  • Web search, scrape, crawl and preprocess content from websites.
  • Support multiple search engines and web scrapers: SearXNG, Tavily, DuckDuckGo, Bing, Google, Zhipu (ζ™Ίθ°±), Exa, Bocha (博ζŸ₯), etc.
  • Local web search (browser search), support multiple search engines: Bing, Google, Baidu, Sogou, etc.
    • Use agent-browser for browser automation.
    • Free, no API keys required.
  • Enabled tools: one_search, one_scrape, one_map, one_extract

Migration from v1.1.0 and Earlier

Breaking Changes in v1.1.0:

  • Firecrawl Removed: The Firecrawl integration has been removed in favor of agent-browser, which provides similar functionality without requiring external API services.
  • New Browser Requirement: You must install Chromium browser (see Prerequisites section).
  • Environment Variables: FIRECRAWL_API_URL and FIRECRAWL_API_KEY are no longer used.

What Changed:

  • one_scrape and one_map now use agent-browser instead of Firecrawl
  • one_extract now preprocesses multi-URL page content for downstream analysis instead of performing built-in LLM extraction
  • All browser-based operations are now handled locally, providing better privacy and no API costs

Migration Steps:

  1. Install Chromium browser (see Prerequisites)
  2. Remove FIRECRAWL_API_URL and FIRECRAWL_API_KEY from your environment variables
  3. Update to the latest version: npm install -g one-search-mcp@latest

Prerequisites

Browser Requirement: This server uses agent-browser for web scraping and local search, which requires a Chromium-based browser.

Good News: The server will automatically detect and use browsers already installed on your system:

  • βœ… Google Chrome
  • βœ… Microsoft Edge
  • βœ… Chromium
  • βœ… Google Chrome Canary

If you don't have any of these browsers installed, you can:

bash
# Option 1: Install Google Chrome (Recommended)
# Download from: https://www.google.com/chrome/

# Option 2: Install Microsoft Edge
# Download from: https://www.microsoft.com/edge

# Option 3: Install Chromium via agent-browser
npx agent-browser install

# Option 4: Install Chromium directly
# Download from: https://www.chromium.org/getting-involved/download-chromium/

Installation

Using Claude Code CLI (Recommended)

bash
# Add to Claude Code with default settings (local search)
claude mcp add one-search-mcp -- npx -y one-search-mcp

# Add with custom search provider (e.g., SearXNG)
claude mcp add one-search-mcp -e SEARCH_PROVIDER=searxng -e SEARCH_API_URL=http://127.0.0.1:8080 -- npx -y one-search-mcp

# Add with Tavily API
claude mcp add one-search-mcp -e SEARCH_PROVIDER=tavily -e SEARCH_API_KEY=your_api_key -- npx -y one-search-mcp

Manual Installation

bash
# Install globally (Optional)
npm install -g one-search-mcp

# Or run directly with npx
npx -y one-search-mcp

Using Docker

Docker image includes all dependencies (Chromium browser) pre-installed, no additional setup required.

Pull the image:

bash
# From GitHub Container Registry
docker pull ghcr.io/yokingma/one-search-mcp:latest

# Or from Docker Hub
docker pull zacma/one-search-mcp:latest

Configure with Claude Desktop:

json
{
  "mcpServers": {
    "one-search-mcp": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "ghcr.io/yokingma/one-search-mcp:latest"],
      "env": {
        "SEARCH_PROVIDER": "local"
      }
    }
  }
}

With custom search provider:

json
{
  "mcpServers": {
    "one-search-mcp": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "SEARCH_PROVIDER=tavily",
        "-e", "SEARCH_API_KEY=your_api_key",
        "ghcr.io/yokingma/one-search-mcp:latest"
      ]
    }
  }
}

Environment Variables

Search Engine:

  • SEARCH_PROVIDER (Optional): The search provider to use, supports searxng, duckduckgo, bing, tavily, google, zhipu, exa, bocha, local, default is local.
  • SEARCH_API_URL (Optional): The URL of the SearxNG API, or Google Custom Search Engine ID for google.
  • SEARCH_API_KEY (Optional): The API key for the search provider, required for tavily, bing, google, zhipu, exa, bocha.
ts
// supported search providers
export type SearchProvider = 'searxng' | 'duckduckgo' | 'bing' | 'tavily' | 'google' | 'zhipu' | 'exa' | 'bocha' | 'local';

Search Provider Configuration

ProviderAPI Key RequiredAPI URL RequiredNotes
localNoNoFree, uses browser automation
duckduckgoNoNoFree, no API key needed
searxngOptionalYesSelf-hosted meta search engine
bingYesNoBing Search API
tavilyYesNoTavily API
googleYesYes (Search Engine ID)Google Custom Search
zhipuYesNoζ™Ίθ°± AI
exaYesNoExa AI
bochaYesNo博ζŸ₯ AI

Configuration for Other MCP Clients

Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

json
{
  "mcpServers": {
    "one-search-mcp": {
      "command": "npx",
      "args": ["-y", "one-search-mcp"],
      "env": {
        "SEARCH_PROVIDER": "local"
      }
    }
  }
}

Cursor

Add to your mcp.json file:

json
{
  "mcpServers": {
    "one-search-mcp": {
      "command": "npx",
      "args": ["-y", "one-search-mcp"],
      "env": {
        "SEARCH_PROVIDER": "local"
      }
    }
  }
}

Windsurf

Add to your ./codeium/windsurf/model_config.json file:

json
{
  "mcpServers": {
    "one-search-mcp": {
      "command": "npx",
      "args": ["-y", "one-search-mcp"],
      "env": {
        "SEARCH_PROVIDER": "local"
      }
    }
  }
}

Self-hosting SearXNG (Optional)

If you want to use SearXNG as your search provider, you can deploy it locally using Docker:

Prerequisites:

  • Docker installed and running (version 20.10.0 or higher)
  • At least 4GB of RAM available

Quick Start:

bash
# Clone SearXNG Docker repository
git clone https://github.com/searxng/searxng-docker.git
cd searxng-docker

# Start SearXNG
docker compose up -d

After deployment, SearXNG will be available at http://127.0.0.1:8080 by default.

Configure OneSearch to use SearXNG:

bash
# Set environment variables
export SEARCH_PROVIDER=searxng
export SEARCH_API_URL=http://127.0.0.1:8080

For more details, see the official SearXNG Docker documentation.

Troubleshooting

Browser not found error

If you see an error like "Browser not found", the server couldn't detect any installed Chromium-based browser. Please install one of the following:

Or install via agent-browser:

bash
npx agent-browser install

License

MIT License - see LICENSE file for details.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "one-search-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "one-search-mcp"
      ],
      "env": {
        "SEARCH_PROVIDER": "searxng",
        "SEARCH_API_URL": "http://127.0.0.1:8080",
        "SEARCH_API_KEY": "YOUR_API_KEY",
        "FIRECRAWL_API_URL": "http://127.0.0.1:3002",
        "FIRECRAWL_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Use One Search MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once One Search is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add One Search as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the One Search MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "one-search": {
      "command": "npx",
      "args": [
        "-y",
        "one-search-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the One Search MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the One Search MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use One Search to help me with this task?
One Search
Sure. I read it.
Here is what I found using One Search.

Frequently asked questions

What is the One Search MCP server used for?

One Search is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use One Search MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use One Search with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use One Search MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With One Search connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect One Search MCP to TypingMind?

One Search runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does One Search MCP provide in TypingMind?

One Search exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use One Search MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If One Search requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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