Introduction
# Google Web Search
## Overview
This skill provides the capability to perform real-time web searches via the Gemini API's `google_search` grounding tool. It is designed to fetch the most current information available on the web to provide grounded, citable answers to user queries.
**Key Features:** - Real-time web search via Gemini API - Grounded responses with verifiable citations - Configurable model selection - Simple Python API
## Usage
This skill exposes the Gemini API's `google_search` tool. It should be used when the user asks for **real-time information**, **recent events**, or requests **verifiable citations**.
### Execution Context
The core logic is in `scripts/example.py`. This script requires the following environment variables:
- **GEMINI_API_KEY** (required): Your Gemini API key - **GEMINI_MODEL** (optional): Model to use (default: `gemini-2.5-flash-lite`)
**Supported Models:** - `gemini-2.5-flash-lite` (default) - Fast and cost-effective - `gemini-3-flash-preview` - Latest flash model - `gemini-3-pro-preview` - More capable, slower - `gemini-2.5-flash-lite-preview-09-2025` - Specific version
### Python Tool Implementation Pattern
When integrating this skill into a larger workflow, the helper script should be executed in an environment where the `google-genai` library is available and the `GEMINI_API_KEY` is exposed.
Example Python invocation structure: ```python from skills.google-web-search.scripts.example import get_grounded_response
# Basic usage (uses default model): prompt = "What is the latest market trend?" response_text = get_grounded_response(prompt) print(response_text)
# Using a specific model: response_text = get_grounded_response(prompt, model="gemini-3-pro-preview") print(response_text)
# Or set via environment variable: import os os.environ["GEMINI_MODEL"] = "gemini-3-flash-preview" response_text = get_grounded_response(prompt) print(response_text) ```
### Troubleshooting
If the script fails: 1. **Missing API Key**: Ensure `GEMINI_API_KEY` is set in the execution environment. 2. **Library Missing**: Verify that the `google-genai` library is installed (`pip install google-generativeai`). 3. **API Limits**: Check the API usage limits on the Google AI Studio dashboard. 4. **Invalid Model**: If you set `GEMINI_MODEL`, ensure it's a valid Gemini model name. 5. **Model Not Supporting Grounding**: Some models may not support the `google_search` tool. Use flash or pro variants.