gemini-search-mcp

gemini-search-mcp

MCP server for web search powered by Google AI Mode (Gemini). Free, unlimited, no API key.

What is this

An MCP server that gives any AI agent (Claude, Cursor, Windsurf, etc.) the ability to search the web in real-time using Google's AI Mode — the same Gemini-powered search that lives in the "AI Mode" tab on Google Search.

Think of it as a free, unlimited alternative to Grok MCP / Tavily / SerpAPI, backed by Google's search index.

Features

• Free: No API key, no subscription, no quota
• Unlimited: 60+ requests/min with zero rate limiting
• Google quality: Powered by Gemini + Google Search (grounded in real web results)
• MCP native: Works with Claude Desktop, Claude Code, Cursor, Windsurf, Cline
• Also ships OpenAI API: /v1/chat/completions for non-MCP clients
• Fast: ~1.5s average response time

Quick Start

pip install -e .

# Optional: install the undetected-chromedriver backend for CAPTCHA probes.
pip install -e '.[undetected]'

MCP Server (for AI agents)

gemini-search-mcp

OpenAI-compatible API

gemini-search --port 8080

MCP Integration

Claude Code

claude mcp add gemini-search -- gemini-search-mcp

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "gemini-search": {
      "command": "gemini-search-mcp",
      "args": [],
      "env": {
        "CDP_URL": "http://127.0.0.1:9222"
      }
    }
  }
}

Cursor / Windsurf

Same pattern — point to gemini-search-mcp as an stdio MCP server.

MCP Tools

Examples

web_search("latest AI regulation news 2026")
→ "The EU AI Act enforcement began on June 1, 2026, requiring..."

web_search("Bitcoin price today")
→ "As of June 30, 2026, Bitcoin is trading at $59,687 USD..."

ask("what is 1847 * 293")
→ "541171"

OpenAI API Usage

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"gemini-search","messages":[{"role":"user","content":"What happened in the news today?"}]}'

Environment Variables

Persistent Chrome profile / CAPTCHA priming

If Google shows /sorry/ CAPTCHA for a fresh temporary profile, prime a persistent profile once in a visible Chrome window, then reuse the same directory in headless mode:

# 1) Visible first run: solve CAPTCHA manually if Google asks.
gemini-search --no-headless --user-data-dir "$HOME/.local/share/gemini-search-mcp/chrome-profile"

# 2) Later runs: reuse the same cookies headlessly.
GEMINI_SEARCH_USER_DATA_DIR="$HOME/.local/share/gemini-search-mcp/chrome-profile" gemini-search

For Windows-side validation from WSL, run the probe with Windows Python through PowerShell so it launches Windows Chrome:

$profile = Join-Path $env:TEMP 'gemini-search-mcp-persistent-profile'
python .\scripts\windows_chrome_profile_probe.py `
  --profile-dir $profile `
  --mode two-phase `
  --out .\headless-reuse-result.json

Success evidence is ok=true and stages.headless_reuse.captcha=false in the JSON output.

undetected-chromedriver CAPTCHA probe

When a normal Chrome subprocess gets a Google /sorry/ CAPTCHA, install the optional backend and run the reusable probe against google.com.hk:

pip install -e '.[undetected]'
python scripts/uc_google_probe.py \
  --proxy socks5://127.0.0.1:7897 \
  --out-json uc-probe.json

Use the backend only when the probe reports ok=true, captcha=false, and successful_for_engine_integration=true.

gemini-search \
  --browser-backend undetected \
  --proxy-server socks5://127.0.0.1:7897 \
  --chromedriver-path /path/to/chromedriver \
  --no-headless

Observed on Windows Chrome for Testing 148 through Clash: headed UC passed (captcha=false and AI Mode tokens present), while headless UC hit Google /sorry/.

How It Works

Google rate-limits by TLS fingerprint quality — not by IP. Automated HTTP clients (curl, requests, httpx) get throttled after a few requests. But a real Chrome browser's fetch() calls are trusted unconditionally.

This tool runs a single real Chrome tab and executes all queries as fetch() inside it over CDP, giving every request an authentic Chrome TLS/HTTP2 fingerprint. Google sees normal browser traffic and applies no rate limits. The optional undetected backend still uses the same CDP query path after launch.

Agent calls web_search("query")
  → Chrome Runtime.evaluate(fetch)
    → Google Search AI Mode (token extraction + folwr endpoint)
      → Parse answer from HTML response
        → Return to agent

Comparison

Docker

docker compose up -d

Requirements

• Python 3.10+
• Chrome, Edge, or Chromium
• Runtime dependencies from pyproject.toml
• Optional: undetected-chromedriver and selenium via pip install -e '.[undetected]'

Limitations

• Requires Chrome/Edge/Chromium installed
• No conversation memory between requests
• Answer extraction relies on Google's DOM structure (may break on updates)
• Streaming is chunked, not per-token

Acknowledgments

• GenericAgent — 本项目核心开发依仗 GA 提供的 AI 能力
• linux.do community

License

MIT