Google Web Search

Enables grounded question answering by automatically executing the Google Search tool within Gemini models. Use when the required information is recent (post knowledge cutoff) or requires verifiable citation.

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.