mcp-webgate

Web search that doesn't wreck your AI's memory.

mcp-webgate is an MCP server that gives your AI clean, bounded web content — across all major AI clients:

• IDEs: Claude Desktop, Claude Code, Zed, Cursor, Windsurf, VSCode
• CLI Agents: Gemini CLI, Claude CLI, custom agents

🌱 A Gentle Introduction

What is mcp-webgate? When your AI uses a standard "fetch URL" tool, it gets the raw HTML of the page — ads, menus, scripts, cookie banners and all. A single news article can dump 200,000 tokens of garbage into the AI's memory, wiping out your entire conversation.

mcp-webgate is a protective filter that sits between your AI and the web:

1. Strips the junk — menus, scripts, ads, footers are removed with surgical HTML parsing; only readable text passes through
2. Hard-caps every response — no page can ever blow up your context window, no matter how big the original was
3. Optionally summarizes — route results through a secondary local LLM that produces a compact Markdown report with citations; your primary AI gets a polished briefing instead of a wall of text

The result: clean, bounded, useful web content — always.

🔬 Real example: what happens under the hood

Searching for "mcp model context protocol" with LLM features on:

Query → LLM expands to 5 search variants → 20 pages found, 13 fetched in parallel

Raw HTML downloaded     5.16 MB   (~1,290,000 tokens)
After cleaning          52.1 KB   (   ~13,000 tokens)  — 99% noise stripped
After LLM summary        5.8 KB   (    ~1,450 tokens)  — structured report with citations

13 sources distilled into ~1,450 tokens. A single naive fetch of just one of those pages (e.g. a security blog at 563 KB) would dump ~140,000 tokens of raw HTML into your AI's context. webgate processes all 13 and delivers a clean briefing that fits in a footnote.

This is an intensive case (5 queries × 5 results). A typical search with 3–5 results still saves 95%+ of context compared to raw fetching — and your AI gets structured, ranked content instead of a wall of HTML soup.

🚀 Quick Start

1. Make sure you have uvx

pip install uv

uvx runs Python tools without installing them permanently. You only need to do this once.

2. Set up a search backend

The easiest option is SearXNG — free, no account, runs locally:

docker run -d -p 8080:8080 --name searxng searxng/searxng

No Docker? Use a cloud backend instead (Brave, Tavily, Exa, SerpAPI) — see Backends.

3. Add webgate to your AI client

See the Integrations table for your specific client. As a quick example, for Claude Desktop:

Open the config file:

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

Add this:

{
  "mcpServers": {
    "webgate": {
      "command": "uvx",
      "args": ["mcp-webgate"],
      "env": {
        "WEBGATE_DEFAULT_BACKEND": "searxng",
        "WEBGATE_SEARXNG_URL": "http://localhost:8080"
      }
    }
  }
}

Restart the client after editing.

4. Ask your AI to search!

Search the web for: latest news on AI regulation

The AI will use webgate_query automatically. You're done.

🔍 How it works

Your question
    ↓
Search backend  (SearXNG / Brave / Tavily / Exa / SerpAPI)
    ↓  [deduplicate URLs, block binary files, filte

... [View full README on GitHub](https://github.com/annibale-x/mcp-webgate#readme)