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▼
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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