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google-web-search

Enables grounded question answering by automatically executing the Google Search tool within Gemini models.

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Installation

npx clawhub@latest install google-web-search

View the full skill documentation and source below.

Documentation

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:

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:

  • Missing API Key: Ensure GEMINI_API_KEY is set in the execution environment.

  • Library Missing: Verify that the google-genai library is installed (pip install google-generativeai).

  • API Limits: Check the API usage limits on the Google AI Studio dashboard.

  • Invalid Model: If you set GEMINI_MODEL, ensure it's a valid Gemini model name.

  • Model Not Supporting Grounding: Some models may not support the google_search tool. Use flash or pro variants.