MCP Serp

MCP Serp

A Model Context Protocol (MCP) server for Google search using SERP API through the AceDataCloud API.

Perform Google searches and get structured results directly from Claude, VS Code, or any MCP-compatible client.

Features

• Web Search - Regular Google web search with structured results
• Image Search - Search for images with URLs and thumbnails
• News Search - Get latest news articles on any topic
• Video Search - Find videos from YouTube and other sources
• Places Search - Search for local businesses and places
• Maps Search - Find locations and geographic information
• Knowledge Graph - Get structured entity information
• Localization - Support for multiple countries and languages
• Time Filtering - Filter results by time range

Quick Start

1. Get API Token

Get your API token from AceDataCloud Platform:

1. Sign up or log in
2. Navigate to Google SERP API
3. Click "Acquire" to get your token

2. Install

# Clone the repository
git clone https://github.com/AceDataCloud/mcp-serp.git
cd mcp-serp

# Install with pip
pip install -e .

# Or with uv (recommended)
uv pip install -e .

3. Configure

# Copy example environment file
cp .env.example .env

# Edit with your API token
echo "ACEDATACLOUD_API_TOKEN=your_token_here" > .env

4. Run

# Run the server
mcp-serp

# Or with Python directly
python main.py

Claude Desktop Integration

Add to your Claude Desktop configuration:

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

{
  "mcpServers": {
    "serp": {
      "command": "mcp-serp",
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Or if using uv:

{
  "mcpServers": {
    "serp": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mcp-serp", "mcp-serp"],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Available Tools

Search Tools

Information Tools

Usage Examples

Basic Web Search

User: Search for information about artificial intelligence

Claude: I'll search for information about AI.
[Calls serp_google_search with query="artificial intelligence"]

News Search with Time Filter

User: What's the latest news about technology?

Claude: I'll search for recent tech news.
[Calls serp_google_news with query="technology", time_range="qdr:d"]

Localized Search

User: Find popular restaurants in Tokyo

Claude: I'll search for restaurants in Tokyo.
[Calls serp_google_places with query="popular restaurants Tokyo", country="jp"]

Image Search

User: Find images of the Northern Lights

Claude: I'll search for aurora borealis images.
[Calls serp_google_images with query="Northern Lights aurora borealis"]

Search Parameters

Search Types

Time Range Filters

Common Country Codes

Common Language Codes

Response Structure

Regular Search Results

• knowledge_graph: Entity information (company, person, etc.)
• answer_box: Direct answers
• organic: Regular search results with title, link, snippet
• people_also_ask: Related questions
• related_searches: Related queries

Image Search Results

• images: Image results with URLs and thumbnails

News Search Results

• news: News articles with source and date

Configuration

Environment Variables

Command Line Options

mcp-serp --help

Options:
  --version          Show version
  --transport        Transport mode: stdio (default) or http
  --port             Port for HTTP transport (default: 8000)

Development

Setup Development Environment

# Clone repository
git clone https://github.com/AceDataCloud/mcp-serp.git
cd mcp-serp

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

# Install with dev dependencies
pip install -e ".[dev,test]"

Run Tests

# Run unit tests
pytest

# Run with coverage
pytest --cov=core --cov=tools

# Run integration tests (requires API token)
pytest tests/test_integration.py -m integration

Code Quality

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy core tools

Build & Publish

# Install build dependencies
pip install -e ".[release]"

# Build package
python -m build

# Upload to PyPI
twine upload dist/*

Project Structure

MCPSerp/
├── core/                   # Core modules
│   ├── __init__.py
│   ├── client.py          # HTTP client for SERP API
│   ├── config.py          # Configuration management
│   ├── exceptions.py      # Custom exceptions
│   └── server.py          # MCP server initialization
├── tools/                  # MCP tool definitions
│   ├── __init__.py
│   ├── search_tools.py    # Search tools
│   └── info_tools.py      # Information tools
├── prompts/                # MCP prompt templates
│   └── __init__.py
├── tests/                  # Test suite
│   ├── conftest.py
│   ├── test_client.py
│   └── test_config.py
├── .env.example           # Environment template
├── .gitignore
├── CHANGELOG.md
├── LICENSE
├── main.py                # Entry point
├── pyproject.toml         # Project configuration
└── README.md

API Reference

This server wraps the AceDataCloud Google SERP API:

• Google SERP API Documentation

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing)
5. Open a Pull Request

License

MIT License - see LICENSE for details.

Links

• AceDataCloud Platform
• Google SERP API
• Model Context Protocol
• MCP Python SDK

Made with love by AceDataCloud