Performs web searches using DuckDuckGo's search engine and returns results with AI-powered summarization in both summary and detailed analysis modes.
Uses Google's Gemini 2.5 Flash AI model to generate intelligent summaries and detailed analyses of web search results.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Web-Scoutfind recent breakthroughs in quantum computing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Web-Scout: AI-Powered Search with LLM Summarization
A FastAPI application that performs web searches using DuckDuckGo and generates AI-powered summaries using Google's Gemini AI.
Features
Web search using DuckDuckGo
AI summarization using Gemini 2.5 Flash
Two output modes: Summary and Detailed analysis
Docker & Docker Compose ready
Secure API key management via environment variables
MCP (Model Context Protocol) support over HTTP
Prerequisites
Docker and Docker Compose installed
Google Gemini API key
Setup
Clone the repository (or navigate to your project directory)
Add your Gemini API key to the
.envfile:GEMINI_API_KEY=your_actual_gemini_api_key_hereBuild and run with Docker Compose:
docker-compose up --build
API Usage
The application will be available at http://localhost:8000
Health Check
curl http://localhost:8000/healthSearch Endpoint
Summary Mode (Default)
curl "http://localhost:8000/search?query=artificial+intelligence"
# or explicitly specify mode=summary
curl "http://localhost:8000/search?query=artificial+intelligence&mode=summary"Detailed Mode
curl "http://localhost:8000/search?query=artificial+intelligence&mode=detailed"Response Format
{
"query": "your search query",
"mode": "summary",
"summary": "AI-generated analysis...",
"sources_used": 10
}MCP Server Integration
Web-Scout can also function as an MCP (Model Context Protocol) server, allowing AI assistants to perform web searches directly through tools.
MCP Features
Web Search Tool: Perform web searches with AI summarization
Dual Mode Support: Both summary and detailed analysis modes
HTTP Transport: MCP over HTTP protocol for client integration
JSON Responses: Structured output for easy integration
MCP Tools Available
Web Search Tool
Name:
web_searchDescription: Perform a web search using DuckDuckGo and generate AI-powered summaries
Parameters:
query(string, required): The search query to performmode(string, optional): Response mode - "summary" or "detailed" (default: "summary")
MCP Server Setup
Web-Scout provides MCP functionality over HTTP, accessible at the /mcp endpoint.
Method 1: Direct FastAPI Server
Install dependencies:
pip install -r requirements.txtSet your Gemini API key:
export GEMINI_API_KEY=your_api_key_hereRun the HTTP server with MCP endpoint:
python main.pyThe MCP endpoint will be available at http://localhost:8000/mcp
Method 2: Docker Container
# Run the HTTP server with Docker (MCP available at /mcp)
docker-compose up --buildOr run standalone container
docker run -p 8000:8000 -e GEMINI_API_KEY=your_api_key_here web-scout
Integrating with AI Tools
To use Web-Scout as an MCP server with AI tools like Claude Desktop or Roo:
Create MCP Configuration:
{
"mcpServers": {
"web-scout": {
"command": "python",
"args": ["-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"],
"env": {
"GEMINI_API_KEY": "your_gemini_api_key_here"
}
}
}
}Configure your AI tool to use the MCP configuration:
For Claude Desktop: Add to
~/Library/Application Support/Claude/claude_desktop_config.jsonFor Roo: Add to the appropriate configuration file
Usage Example:
Can you search for the latest news about artificial intelligence?The AI tool will use the Web-Scout MCP server (via the /mcp endpoint) to perform the search and provide summarized results.
Development
Local Development (without Docker)
# Install dependencies
pip install -r requirements.txt
# Set your API key
export GEMINI_API_KEY=your_api_key_here
# Run the application
uvicorn main:app --reloadUsing Docker Compose
# Build and run
docker-compose up --build
# Run in background
docker-compose up -d --build
# Stop the application
docker-compose down
# View logs
docker-compose logs -f web-scoutConfiguration
Environment Variables
GEMINI_API_KEY: Your Google Gemini API key (required)
Docker Configuration
Port: 8000
Container name: web-scout
Health check: Automatic health monitoring
Security Notes
The
.envfile is ignored by Git and should never be committedAPI keys are mounted securely via Docker Compose volumes
The application uses health checks for monitoring
Error Handling
Returns proper HTTP status codes
Includes detailed error messages
Handles missing API keys and invalid parameters gracefully
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Resources
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Looking for Admin?
If you are the server author, to access and configure the admin panel.