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

Related MCP server: SerpApi MCP Server

🚀 Installation

1. Clone the repository:

git clone https://github.com/yourusername/serper-search-server.git
cd serper-search-server

2. Install dependencies:

pnpm install

3. 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

• Serper API for providing the Google search capabilities

• Model Context Protocol for the MCP framework

• PostHog for analytics capabilities