MCP Google Custom Search Server
# 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.
## š 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
## š Prerequisites
Before you begin, ensure you have:
1. A Google Cloud Project with Custom Search API enabled
- Visit [Google Cloud Console](https://console.cloud.google.com)
- Enable the Custom Search API
- Create API credentials
2. A Custom Search Engine ID
- Visit [Programmable Search Engine](https://programmablesearchengine.google.com/)
- Create a new search engine
- Get your Search Engine ID
3. Local development requirements:
- Node.js (v18 or higher)
- npm (comes with Node.js)
## š Quick Start
1. Clone the repository:
```bash
git clone https://github.com/limklister/mcp-google-custom-search-server.git
cd mcp-google-custom-search-server
```
2. Install dependencies:
```bash
npm install
```
3. Create a .env file:
```bash
GOOGLE_API_KEY=your-api-key
GOOGLE_SEARCH_ENGINE_ID=your-search-engine-id
```
4. Build the server:
```bash
npm run build
```
5. Start the server:
```bash
npm start
```
## š§ Configuration
### Environment Variables
| Variable | Description | Required |
| ----------------------- | --------------------------------- | -------- |
| GOOGLE_API_KEY | Your Google Custom Search API key | Yes |
| GOOGLE_SEARCH_ENGINE_ID | Your Custom Search Engine ID | Yes |
### Claude Desktop Integration
Add this configuration to your Claude Desktop config file (typically located at `~/Library/Application Support/Claude/claude_desktop_config.json`):
```json
{
"mcpServers": {
"google-search": {
"command": "node",
"args": [
"/absolute/path/to/mcp-google-custom-search-server/build/index.js"
],
"env": {
"GOOGLE_API_KEY": "your-api-key",
"GOOGLE_SEARCH_ENGINE_ID": "your-search-engine-id"
}
}
}
}
```
## š 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
- Default: 5
- Maximum: 10
**Example Response:**
```
Result 1:
Title: Example Search Result
URL: https://example.com
Description: This is an example search result description
---
Result 2:
...
```
## š ļø Development
### Project Structure
```
mcp-google-custom-search-server/
āāā src/
ā āāā index.ts # Main server implementation
āāā build/ # Compiled JavaScript output
āāā .env # Environment variables
āāā package.json # Project dependencies and scripts
āāā tsconfig.json # TypeScript configuration
āāā README.md # Project documentation
```
### Available Scripts
- `npm run build`: Compile TypeScript to JavaScript
- `npm start`: Start the MCP server
- `npm run dev`: Watch mode for development
### Testing
1. Using MCP Inspector:
```bash
npx @modelcontextprotocol/inspector node build/index.js
```
2. Manual testing with example queries:
```bash
# After starting the server
{"jsonrpc":"2.0","id":1,"method":"callTool","params":{"name":"search","arguments":{"query":"example search"}}}
```
<a href="https://glama.ai/mcp/servers/y1s99uqqq6">
<img width="380" height="200" src="https://glama.ai/mcp/servers/y1s99uqqq6/badge" alt="Google Custom Search Server MCP server" />
</a>
[](https://mseep.ai/app/limklister-mcp-google-custom-search-server)
## š License
This project is licensed under the MIT License - see the LICENSE file for details.
## š Acknowledgments
- Built with [Model Context Protocol (MCP)](https://github.com/anthropics/model-context-protocol)
- Uses Google's Custom Search API
- Inspired by the need for better search capabilities in LLM applications
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is singular and clearly defined, eliminating any ambiguity in tool selection.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The tool name 'search' is straightforward and follows a simple verb pattern appropriate for its function.
A single tool is too few for a server with a purpose like web search, which could benefit from additional tools such as filtering, pagination, or advanced search options. This minimal set feels thin and limits functionality.
The tool surface is severely incomplete for a web search domain. While the basic search function is covered, there are obvious gaps such as no tools for result refinement, handling multiple pages, or accessing search metadata, which could lead to agent failures in complex tasks.