mcp-websearch

Web Search MCP Server

A Model Context Protocol (MCP) server implementation for searching content from various Indonesian news portals and Wikipedia. This application can be deployed as a Google Cloud Function.

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications - it provides a standardized way to connect AI models to different data sources and tools.

Key Benefits

• A growing list of pre-built integrations that your LLM can directly plug into
• Flexibility to switch between LLM providers and vendors
• Best practices for securing your data within your infrastructure

Architecture Overview

MCP follows a client-server architecture where a host application can connect to multiple servers:

• MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP
• MCP Clients: Protocol clients that maintain 1:1 connections with servers
• MCP Servers: Lightweight programs that expose specific capabilities through the standardized Model Context Protocol
• Data Sources: Both local (files, databases) and remote services (APIs) that MCP servers can access

Core MCP Concepts

MCP servers can provide three main types of capabilities:

• Resources: File-like data that can be read by clients (like API responses or file contents)
• Tools: Functions that can be called by the LLM (with user approval)
• Prompts: Pre-written templates that help users accomplish specific tasks

System Requirements

• Python 3.11 or higher
• Google Cloud SDK
• Google Cloud account with billing enabled
• Serper API key for web search functionality

Local Development Setup

1. Clone the repository:

git clone git@github.com:addhe/mcp-websearch.git
cd mcp-websearch

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Create a .env file with your Serper API key:

SERPER_API_KEY=your_api_key_here

5. Run locally:

python main.py

VS Code Integration

1. Create .vscode/mcp.json with the following configuration:

{
    "servers": {
        "my-mcp-server-websearch": {
            "type": "http",
            "url": "https://your-cloud-function-url.cloudfunctions.net/websearch"
        }
    }
}

2. Install any MCP-compatible extension in VS Code to use the search functionality.

Deploying to Google Cloud Functions

Prerequisites

1. Install the Google Cloud SDK
2. Initialize the SDK and set your project:

gcloud init

Deployment Steps

1. Make sure your SERPER_API_KEY is set in your environment:

export SERPER_API_KEY=your_api_key_here

2. Run the deployment script:

./deploy.sh

The script will:

• Enable required Google Cloud APIs
• Deploy the function with Python 3.11 runtime
• Configure the function with your SERPER_API_KEY

Function Usage

When using the MCP protocol, send HTTP POST requests with this JSON body format:

{
    "name": "get_docs",
    "parameters": {
        "query": "your search query",
        "library": "detik|liputan6|cnn|wikipedia"
    }
}

Example response:

{
    "result": "Text content from the searched portal",
    "type": "success"
}

Available portals:

• detik (news.detik.com/berita)
• liputan6 (liputan6.com/news)
• cnn (cnnindonesia.com/nasional)
• wikipedia (www.wikipedia.org)

Technical Details

• Runtime: Python 3.11
• HTTP Trigger with MCP protocol support
• Async implementation using httpx and aiohttp
• BeautifulSoup4 for HTML parsing
• Memory: 256MB (default)
• Timeout: 60 seconds (default)

Environment Variables

• SERPER_API_KEY: Your Google Serper API key for web search functionality (required)

Error Handling

The function returns appropriate HTTP status codes:

• 200: Successful request with MCP format response
• 400: Missing or invalid parameters
• 404: Function name not found
• 500: Server error or timeout

Error responses follow MCP format:

{
    "error": "Error message",
    "type": "error"
}

Limitations

• Maximum timeout: 60 seconds
• Maximum memory: 256MB (can be increased if needed)
• Rate limits apply based on your Serper API plan
• Currently supports only Indonesian news portals and Wikipedia

Contributing

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

License

This project is licensed under the MIT License. See the file for details.