Which integrations are available for this server?

Provides web search capabilities using DuckDuckGo, returning extracted data and snippets. Enables searching GitHub repositories and retrieving repository information such as stars, forks, languages, and topics. Extracts movie and show details including titles, ratings, cast, and genres from IMDb. Allows searching Reddit for posts and retrieving scores, comments, and subreddit information. Extracts Wikipedia infoboxes, summaries, and key facts from articles. Supports searching YouTube and fetching video details such as titles, channels, views, and duration.

How do I use mcp-internet?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-internet what are the upcoming Bollywood movies in 2026?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

mcp-internet

by Sonesh-2202

Overview Schema Related Servers Score Discussions

Python

Remote

🌐 MCP Internet Server v3.0

Python 3.10+ MCP License: MIT Multi-Platform LM Studio

Give your local LLMs the power of the internet — with real data extraction, not just links! 🚀

An open-source Model Context Protocol (MCP) server providing 25 internet tools for local Large Language Models. The v3.0 upgrade adds intelligent search with auto-extraction, multi-source aggregation, result caching, and structured output — making your AI return actual data instead of raw links.

Seamlessly deployable across Windows, macOS, and Linux. No API keys required!

🆕 What's New in v3.0

Feature	Before (v2.0)	After (v3.0)
Search Results	Links + snippets only	Full data extraction from pages
Tool Calls Needed	5-10+ per query	1-2 per query
Output Format	Plain text	Markdown tables, bullet points
Caching	None	SQLite with TTL (30min-24h)
Rate Limiting	None	Per-domain throttling
Multi-Source	Manual per source	Auto-aggregation from top results
Content Extraction	Generic text only	Domain-aware (Wikipedia, IMDb, LinkedIn)

New Tools

smart_search — 🌟 Searches + scrapes + extracts + formats in one call
deep_search — Like smart_search but examines more sources
clear_cache — Manage the result cache

Related MCP server: Local-MCP-server

✨ Key Features

🧠 Smart Search: One tool call returns extracted data — movie lists, profiles, technical docs — not just links.
🔄 Auto-Extraction: Automatically scrapes top results and extracts structured data (JSON-LD, tables, meta tags).
📊 Structured Output: Results come as markdown tables, bullet points, and organized sections.
⚡ Cached Results: Repeated queries are served from SQLite cache in milliseconds.
🛡️ Rate Limiting: Per-domain request throttling prevents IP bans.
🔀 UA Rotation: Pool of 7 browser user-agents for stealth.
🌐 Domain-Aware: Special extractors for Wikipedia, IMDb, LinkedIn, and more.
🔒 Zero Config: No API keys, no external subscriptions, no setup hassle.

🛠️ Available Tools (25 Total)

Category	Tools
🌟 Smart Search	`smart_search`, `deep_search`
🔍 Basic Search	`search_web`, `quick_lookup`, `search_site`
📄 Reading	`read_webpage`, `read_pdf`
📰 News/Social	`get_news`, `search_reddit`, `search_twitter`
🎬 YouTube	`search_youtube`, `get_video_info`
💻 GitHub	`search_github`, `get_repo_info`
🌤️ Info / Utils	`get_weather`, `get_current_time`, `translate_text`, `calculate`
🔗 URLs/IP	`shorten_url`, `get_my_ip`, `geolocate_ip`
📱 Generation	`generate_qr`, `generate_wifi_qr`, `send_email`
🗄️ Cache	`clear_cache`

🚀 Quick Start

Prerequisites

Python 3.10+
uv (recommended) or pip

Installation

# 1. Clone the repository
git clone https://github.com/Sonesh-2202/mcp-internet.git
cd mcp-internet

# 2. Install dependencies
uv sync

LM Studio Configuration

Add to your LM Studio mcp.json:

{
  "mcpServers": {
    "mcp-internet": {
      "command": "uv",
      "args": ["run", "mcp-internet"],
      "cwd": "/absolute/path/to/mcp-internet"
    }
  }
}

(Replace /absolute/path/to/mcp-internet with your actual project path)

📖 Detailed OS Guides

For complete, step-by-step instructions for Windows, macOS, and Linux, see the 👉 Multi-Platform Usage Guide.

💡 How It Works: smart_search vs search_web

Before (v2.0): Multiple tool calls, manual scraping

User: "What are the upcoming Bollywood movies in 2026?"

Tool call 1: search_web("upcoming Bollywood movies 2026")
→ Returns 10 links with snippets

Tool call 2: read_webpage("https://www.imdb.com/...")
→ Returns raw text

Tool call 3: read_webpage("https://www.wikipedia.org/...")
→ Returns raw text

AI must manually piece together the answer from raw text.

After (v3.0): One tool call, structured data

User: "What are the upcoming Bollywood movies in 2026?"

Tool call 1: smart_search("upcoming Bollywood movies 2026")
→ Returns:
  - Extracted movie list with titles, dates, genres
  - Markdown tables with ratings
  - Data from multiple sources combined
  - All in one response, ready to present

🏗️ Architecture

mcp-internet/
├── pyproject.toml              # Dependencies & Config
├── README.md                   # This file
├── MULTI_PLATFORM_GUIDE.md     # OS-specific setup guide
└── src/mcp_internet/
    ├── server.py               # Entry point — 25 MCP tools
    ├── tools/
    │   ├── smart_search.py     # 🌟 Intelligent search + extraction
    │   ├── search.py           # Basic DuckDuckGo search
    │   ├── webpage.py          # Page reader with structured extraction
    │   ├── news.py             # News headlines
    │   ├── youtube.py          # YouTube search & video info
    │   ├── github.py           # GitHub repos & users
    │   ├── reddit.py           # Reddit search
    │   ├── twitter.py          # Twitter/X via Nitter
    │   └── ...                 # weather, time, translate, math, etc.
    └── utils/
        ├── http_client.py      # Async HTTP with UA rotation & rate limiting
        ├── cache.py            # SQLite TTL cache
        └── extractors.py       # Domain-specific data extractors

Data Flow

User Query → smart_search
    ├── 1. Check cache (return if fresh)
    ├── 2. Optimize query & classify (person/movie/news/tech/general)
    ├── 3. Search DuckDuckGo (HTML scraping + ddgs fallback)
    ├── 4. Prioritize results by domain authority
    ├── 5. Scrape top 3 pages in parallel (asyncio.gather)
    ├── 6. Apply domain-specific extractors
    │       ├── Wikipedia → infobox, summary, key facts
    │       ├── IMDb → titles, ratings, cast, genres
    │       ├── LinkedIn → name, role, skills, experience
    │       └── Generic → JSON-LD, OpenGraph, tables
    ├── 7. Aggregate & format as structured markdown
    ├── 8. Cache result
    └── 9. Return comprehensive response

🔧 Supported Data Sources

Source	What's Extracted	Extractor Type
Wikipedia	Infobox, summary, key facts	Domain-specific
IMDb	Movie/show details, ratings, cast, genres	Domain-specific (JSON-LD)
LinkedIn	Name, role, skills, experience, education	Domain-specific (Googlebot UA)
GitHub	Repos, stars, forks, languages, topics	API-based
Reddit	Posts, scores, comments, subreddit info	JSON API
YouTube	Video titles, channels, views, duration	HTML parsing
Any website	JSON-LD, OpenGraph, HTML tables, main text	Generic fallback

⚡ Performance

Metric	Value
Cache hit	< 50ms
Simple search	1-3 seconds
Smart search (3 sources)	3-8 seconds
Deep search (5+ sources)	5-15 seconds
Rate limiting	2 req/sec per domain
UA rotation	7 browser user-agents

🧪 Testing

# Run the integration test suite
uv run python test_v3.py

Recommended LM Studio Test Queries:

"What are the upcoming Bollywood movies in 2026?" — Should return movie lists
"Search the web for Sundar Pichai" — Should return profile data
"Latest albums by Taylor Swift" — Should return music data
"Python asyncio tutorial" — Should return technical content

📄 License & Contributing

This project is licensed under the MIT License - See LICENSE for details.

Contributions, bug reports, and feature requests are highly welcome!

Made with ❤️ for the Local Developer & LLM community

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

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