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Google Web Search

Google Web Search

by theoseo · v1.0.0

Content Creation
ClawHub
7.5
/ 10
1 evaluations
3.3k Downloads

Overview

Provides real-time, grounded web search by calling Google’s Gemini `google_search` tool via the `google-generativeai` Python client and returning citation-backed answers to user prompts.

Key Advantages

1.Enables access to fresh, post–knowledge-cutoff information through Google web search.
2.Uses Gemini grounding to produce answers with verifiable citations instead of raw link dumps.
3.Very simple Python API (`get_grounded_response(prompt, model=...)`) with sensible defaults and environment-variable configuration.
4.Supports multiple Gemini models (flash/pro/preview variants) so users can trade off speed vs capability.
5.Clear troubleshooting guidance for common operational issues (missing API key, library, invalid model, quota).

Use Cases

  • Answering questions about very recent events, news, or time-sensitive data that exceed the base model’s knowledge cutoff.
  • Retrieving up-to-date reference information (market trends, current prices, recent research, policy changes) with citations.
  • Providing grounded, source-linked responses in applications that must justify claims (reports, dashboards, assistants).
  • Augmenting an existing LLM workflow with a simple external web-search step via Python scripting.
  • Building prototypes that need quick, real-time web lookup without implementing custom search APIs.

Evaluation Scores

7.5
/ 10
Reliability
6.7
Functionality
8.2
Usability
8.5
Safety
7.0
Performance
7.4
Compatibility
6.8

Based on 1 evaluation · Latest: 3/19/2026

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Evaluation History (1)

7.5/103/19/2026
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OS: linux-x64LLM: moonshotai/kimi-k2.5
**Judgment:** Solid, focused skill that wraps Google’s Gemini `google_search` grounding tool into a simple Python API for real-time, citation-backed web search. Well-suited for adding fresh web knowledge to workflows, but depends heavily on external configuration (API key, library, correct model) and the stability of the Gemini ecosystem. **Strengths & Benefits** - **Fresh information:** Can answer questions about events and data beyond the base model’s knowledge cutoff. - **Grounded outputs:** Uses Gemini’s google_search grounding to give responses with citations, improving trust and verifiability. - **Straightforward integration:** Single function (`get_grounded_response`) with default model and optional `GEMINI_MODEL` env var. - **Decent documentation:** Explains required env vars, supported models, and common failure modes. **Key Risks & Limitations** - **External dependency:** Requires a working Gemini API key, internet access, and the `google-generativeai` library; failures or API changes will break the skill. - **Model/version fragility:** Uses specific model names (including preview variants) that may change or be deprecated, impacting reliability. - **Privacy considerations:** User queries are sent to Google; not suitable for highly sensitive data unless that data can safely leave the environment. - **No fine-grained control:** The skill exposes high-level search+answering, not low-level search result control or structured result schemas. **Recommended Scenarios** - You need **recent or rapidly changing information** (news, markets, product updates) with **sources included**. - You can **safely send user queries to Google** and manage a Gemini API key and quotas. - You want a **minimal-effort way** to bolt web search onto an LLM workflow via Python without building your own search tooling. **Use with Caution When** - Operating in **strict privacy/compliance environments** where external API calls are constrained. - You need **hard reliability guarantees** or long-term stability against API/model changes. - You require **structured search results or custom ranking**, which this skill does not provide out of the box.

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