google-web-search – OpenClaw Skill

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name: google-web-search description: 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.

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:

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.

Google Web Search (Gemini Grounding)

Real-time web search using Gemini API's google_search grounding tool.

Features

• ✅ Real-time web search with grounded citations
• ✅ Natural language answers (no JSON parsing needed)
• ✅ Configurable Gemini model selection
• ✅ Simple Python API

Quick Start

1. Set API Key

export GEMINI_API_KEY=your_key_here

Get your API key at Google AI Studio

2. Use in OpenClaw

from skills.google-web-search.scripts.example import get_grounded_response

# Ask a question
answer = get_grounded_response("What is the weather in Seoul today?")
print(answer)
# Output: Natural language answer with citations

3. Optional: Change Model

export GEMINI_MODEL=gemini-3-pro-preview

Supported models:

• gemini-2.5-flash-lite (default) - Fast & cheap
• gemini-3-flash-preview - Latest flash
• gemini-3-pro-preview - More capable

Use Cases

• 📰 Real-time news and events
• 💹 Current prices (stocks, crypto, etc.)
• 🌤️ Weather forecasts
• 📊 Latest statistics and data
• 🔍 Any information requiring recent sources

How It Works

This skill uses Gemini's grounding with Google Search tool, which:

1. Executes a Google search for your query
2. Processes the search results
3. Generates a natural language answer
4. Includes verifiable citations

Key advantage: You get curated answers instead of raw search results.

Requirements

• Python ≥ 3.11
• google-genai ≥ 1.50.0
• pydantic-settings ≥ 2.0.0

License

Same as OpenClaw project