MCP Google Custom Search Server
A Model Context Protocol (MCP) server that provides web search capabilities through Google's Custom Search API. This server enables Language Learning Models (LLMs) to perform web searches using a standardized interface.
Features
- Seamless integration with Google Custom Search API
- Model Context Protocol (MCP) compliant server implementation
- Type-safe implementation using TypeScript
- Environment variable configuration
- Input validation using Zod
- Configurable search results (up to 10 per query)
- Formatted search results including titles, URLs, and descriptions
- Error handling and validation
- Compatible with Claude Desktop and other MCP clients
Prerequisites
Before you begin, ensure you have:
- A Google Cloud Project with Custom Search API enabled
- A Custom Search Engine ID
- Local development requirements:
Quick Start
- Clone the repository:
- Install dependencies:
- Create a .env file:
- Build the server:
- Start the server:
Configuration
Environment Variables
[object Object] | [object Object] | [object Object] |
[object Object] | [object Object] | [object Object] |
[object Object] | [object Object] | [object Object] |
Claude Desktop Integration
Add this configuration to your Claude Desktop config file (typically located at
~/Library/Application Support/Claude/claude_desktop_config.json):API Reference
Available Tools
search
Performs a web search using Google Custom Search API.
Parameters:
query(string, required): The search query to execute
numResults(number, optional): Number of results to return
Example Response:
Development
Project Structure
Available Scripts
npm run build: Compile TypeScript to JavaScript
npm start: Start the MCP server
npm run dev: Watch mode for development
Testing
- Using MCP Inspector:
- Manual testing with example queries:
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Built with Model Context Protocol (MCP)
- Uses Google's Custom Search API
- Inspired by the need for better search capabilities in LLM applications
MCP Google Custom Search Server
A Model Context Protocol (MCP) server that provides web search capabilities through Google's Custom Search API. This server enables Language Learning Models (LLMs) to perform web searches using a standardized interface.
Features
- Seamless integration with Google Custom Search API
- Model Context Protocol (MCP) compliant server implementation
- Type-safe implementation using TypeScript
- Environment variable configuration
- Input validation using Zod
- Configurable search results (up to 10 per query)
- Formatted search results including titles, URLs, and descriptions
- Error handling and validation
- Compatible with Claude Desktop and other MCP clients
Prerequisites
Before you begin, ensure you have:
- A Google Cloud Project with Custom Search API enabled
- A Custom Search Engine ID
- Local development requirements:
Quick Start
- Clone the repository:
- Install dependencies:
- Create a .env file:
- Build the server:
- Start the server:
Configuration
Environment Variables
[object Object] | [object Object] | [object Object] |
[object Object] | [object Object] | [object Object] |
[object Object] | [object Object] | [object Object] |
Claude Desktop Integration
Add this configuration to your Claude Desktop config file (typically located at
~/Library/Application Support/Claude/claude_desktop_config.json):API Reference
Available Tools
search
Performs a web search using Google Custom Search API.
Parameters:
query(string, required): The search query to execute
numResults(number, optional): Number of results to return
Example Response:
Development
Project Structure
Available Scripts
npm run build: Compile TypeScript to JavaScript
npm start: Start the MCP server
npm run dev: Watch mode for development
Testing
- Using MCP Inspector:
- Manual testing with example queries:
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Built with Model Context Protocol (MCP)
- Uses Google's Custom Search API
- Inspired by the need for better search capabilities in LLM applications