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Serper Search

STDIO

MCP server providing Google search capabilities through Serper API with AI-powered research.

Serper Search MCP Server

A Model Context Protocol server that provides Google search capabilities through the Serper API, along with an AI-powered Deep Research tool. This server enables easy integration of search and research functionality into your MCP-enabled applications.

✨ Features

  • 🌐 Powerful Google search integration through Serper API
  • 🔄 Rich search response data including:
    • Knowledge Graph information
    • Organic search results
    • "People Also Ask" questions
    • Related searches
  • 🧠 AI-powered Deep Research tool:
    • Performs multi-step, iterative research
    • Generates sub-queries to explore topics thoroughly
    • Synthesizes information from multiple sources
    • Provides citations for all information
    • Adjustable research depth levels
    • Built-in quality metrics for continuous improvement
  • 🛠 Configurable search parameters:
    • Country targeting
    • Language selection
    • Result count customization
    • Autocorrect options
  • 🔒 Secure API key handling
  • ⚡️ Rate limiting and caching support
  • 📝 TypeScript support with full type definitions
  • 📊 Integrated performance metrics for result optimization

🚀 Installation

  1. Clone the repository:
git clone https://github.com/yourusername/serper-search-server.git cd serper-search-server
  1. Install dependencies:
pnpm install
  1. Build the server:
pnpm run build

⚙️ Configuration

  1. Get your Serper API key from [Serper.dev](https://serper.de

  2. Create a .env file in the root directory:

# Required SERPER_API_KEY=your_api_key_here # Optional - Advanced Quality Metrics Configuration (pre-configured by default) USAGE_METRICS_KEY=your-custom-metrics-key # Optional USAGE_PROJECT_ID=your-custom-project-id # Optional METRICS_ENDPOINT=https://your-custom-host.com # Optional DISABLE_METRICS=false # Not recommended

See TELEMETRY.md for detailed information about:

  • Quality metrics collection
  • Performance monitoring
  • Usage analytics
  • Dashboard setup
  • Continuous improvement

🔌 Integration

Claude Desktop

Add the server config to your Claude Desktop configuration:

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

{ "mcpServers": { "serper-search-server": { "command": "/path/to/serper-search-server/build/index.js", "env": { "SERPER_API_KEY": "your_api_key_here" } } } }

🛠 Usage

Search Tool

The server provides a powerful search tool with the following parameters:

{ "query": string, // Search query "numResults"?: number, // Number of results (default: 10, max: 100) "gl"?: string, // Country code (e.g., "us", "uk") "hl"?: string, // Language code (e.g., "en", "es") "autocorrect"?: boolean, // Enable autocorrect (default: true) "type"?: "search" // Search type (more types coming soon) }

Deep Research Tool

For more comprehensive research needs, the server provides a deep research tool that performs multi-step research with the following parameters:

{ "query": string, // Research query or question "depth"?: "basic" | "standard" | "deep", // Research depth (default: "standard") "maxSources"?: number // Maximum sources to include (default: 10) }

The deep research tool:

  • Breaks down complex queries into focused sub-queries
  • Executes multiple searches to gather comprehensive information
  • Uses AI to synthesize information from multiple sources
  • Formats results with proper citations and references
  • Adapts its research strategy based on intermediate results
  • Collects anonymous quality metrics to improve search results

Depth Levels:

  • basic: Quick overview (3-5 sources, ~5 min) Good for: Simple facts, quick definitions, straightforward questions
  • standard: Comprehensive analysis (5-10 sources, ~10 min) Good for: Most research needs, balanced depth and speed
  • deep: Exhaustive research (10+ sources, ~15-20 min) Good for: Complex topics, academic research, thorough analysis

Search Tool Example Response

The search results include rich data:

{ "searchParameters": { "q": "apple inc", "gl": "us", "hl": "en", "autocorrect": true, "type": "search" }, "knowledgeGraph": { "title": "Apple", "type": "Technology company", "website": "http://www.apple.com/", "description": "Apple Inc. is an American multinational technology company...", "attributes": { "Headquarters": "Cupertino, CA", "CEO": "Tim Cook (Aug 24, 2011–)", "Founded": "April 1, 1976, Los Altos, CA" } }, "organic": [ { "title": "Apple", "link": "https://www.apple.com/", "snippet": "Discover the innovative world of Apple...", "position": 1 } ], "peopleAlsoAsk": [ { "question": "What does Apple Inc mean?", "snippet": "Apple Inc., formerly Apple Computer, Inc....", "link": "https://www.britannica.com/topic/Apple-Inc" } ], "relatedSearches": [ { "query": "Who invented the iPhone" } ] }

🔍 Response Types

Knowledge Graph

Contains entity information when available:

  • Title and type
  • Website URL
  • Description
  • Key attributes

Organic Results

List of search results including:

  • Title and URL
  • Snippet (description)
  • Position in results
  • Sitelinks when available

People Also Ask

Common questions related to the search:

  • Question text
  • Answer snippet
  • Source link

Related Searches

List of related search queries users often make.

📊 Quality Metrics

The Deep Research tool includes integrated quality metrics:

  • Research process metrics
  • Performance monitoring
  • Issue tracking
  • Usage patterns
  • Result quality indicators

See TELEMETRY.md for detailed information about the metrics collected to improve search quality.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

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

🙏 Acknowledgments

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