search-web

Google Search (Gemini)

yukukotani

by yukukotani

Access Google Search via Gemini's native API for reliable info and automatic MLA citations. Ideal for developers and ref

Provides Google Search functionality through Gemini's native grounding capabilities, delivering search results with automatic source citations and grounding metadata for reliable information retrieval.

github stars

76

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Built-in source citationsUses Gemini's native groundingSupports both AI Studio and Vertex AI

best for

  • / Researchers needing cited web information
  • / AI applications requiring real-time search
  • / Content creators verifying current information
  • / Developers building search-enabled assistants

capabilities

  • / Search Google using Gemini's grounding feature
  • / Retrieve real-time web search results
  • / Provide automatic source citations
  • / Access grounding metadata for results
  • / Connect via Google AI Studio or Vertex AI

what it does

Provides Google Search functionality through Gemini's grounding capabilities with automatic source citations and metadata for reliable information retrieval.

about

Google Search (Gemini) is a community-built MCP server published by yukukotani that provides AI assistants with tools and capabilities via the Model Context Protocol. Access Google Search via Gemini's native API for reliable info and automatic MLA citations. Ideal for developers and ref It is categorized under search web.

how to install

You can install Google Search (Gemini) in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

Apache-2.0

Google Search (Gemini) is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP Gemini Google Search

日本語

A Model Context Protocol (MCP) server that provides Google Search functionality using Gemini's built-in Grounding with Google Search feature.

This project is inspired by the GoogleSearch tool from gemini-cli.

Features

  • Uses Gemini's built-in Grounding with Google Search feature
  • Provides real-time web search results with source citations
  • Compliant with MCP standard protocol
  • Supports stdio transport
  • Supports both Google AI Studio and Vertex AI

Requirements

  • Node.js 18 or later
  • Google AI Studio API key (Get one here) or Google Cloud Project with Vertex AI enabled

Installation

npm install -g mcp-gemini-google-search

Usage

Environment Variables

# For Google AI Studio (default)
export GEMINI_API_KEY="your-api-key-here"
export GEMINI_MODEL="gemini-2.5-flash"  # Optional (default: gemini-2.5-flash)

# For Vertex AI
export GEMINI_PROVIDER="vertex"
export VERTEX_PROJECT_ID="your-gcp-project-id"
export VERTEX_LOCATION="us-central1"  # Optional (default: us-central1)
export GEMINI_MODEL="gemini-2.5-flash"  # Optional (default: gemini-2.5-flash)

Claude Code Configuration

You can set up this MCP server in Claude Code using the CLI:

For Google AI Studio

# Add to user scope (available across all projects)
claude mcp add gemini-google-search \
  -s user \
  -e GEMINI_API_KEY="your-api-key-here" \
  -e GEMINI_MODEL="gemini-2.5-flash" \
  -- npx mcp-gemini-google-search

# Or add to project scope to share with your team
claude mcp add gemini-google-search \
  -s project \
  -e GEMINI_API_KEY="your-api-key-here" \
  -e GEMINI_MODEL="gemini-2.5-flash" \
  -- npx mcp-gemini-google-search

For Vertex AI

# Add to user scope (available across all projects)
claude mcp add gemini-google-search \
  -s user \
  -e GEMINI_PROVIDER="vertex" \
  -e VERTEX_PROJECT_ID="your-gcp-project-id" \
  -e VERTEX_LOCATION="us-central1" \
  -e GEMINI_MODEL="gemini-2.5-flash" \
  -- npx mcp-gemini-google-search

# Or add to project scope to share with your team
claude mcp add gemini-google-search \
  -s project \
  -e GEMINI_PROVIDER="vertex" \
  -e VERTEX_PROJECT_ID="your-gcp-project-id" \
  -e VERTEX_LOCATION="us-central1" \
  -e GEMINI_MODEL="gemini-2.5-flash" \
  -- npx mcp-gemini-google-search

Windows Users

On Windows, wrap the npx command with cmd /c:

claude mcp add gemini-google-search \
  -e GEMINI_API_KEY="your-api-key-here" \
  -- cmd /c npx mcp-gemini-google-search

Available Tools

google_search

Search Google for information.

Parameters:

  • query (string, required): Search query

Example:

latest TypeScript features
<details> <summary>Example Response</summary>
It appears you're asking about the latest features in TypeScript. Here's a summary of recent updates and key features, based on the provided search results:

**Key Features in Recent TypeScript Updates:**

*   **Satisfies Operator:** This operator lets you specify that a value conforms to a specific type without fully enforcing it.[1,2]
*   **Const Type Parameters:** Using `const` with type parameters provides more precision with function generics, helping specify literal types and prevent unwanted transformations.[2] This ensures arrays are treated as immutable, maintaining their literal types.[2]
*   **Improved Enum Types:** Enums are more robust, especially `const enum`, which optimizes enums by inlining their values at compile time.[2] From version 5.0, all enums are treated as a type union, even with calculated values.[1]
*   **Template Literal Types:** Template literal types are more expressive, allowing you to create types that build on literals, similar to JavaScript template strings.[2]
*   **Unions and Intersections with Discriminated Unions:** TypeScript offers better handling for union and intersection types, which are frequently used to build flexible types.[2] Discriminated unions allow you to create complex structures with ease and clear type guards.[2]
*   **New ECMAScript Set Methods:** Support for new methods like `union`, `intersection`, and `difference` for more powerful set operations.[3]

**TypeScript 5.8 Highlights (March 2025):**

*   **Module Node18 Flag:** Provides a stable reference point for users fixed on Node.js 18, without incorporating certain behaviors of `--module nodenext`.[4]
*   **Optimizations:** Introduces optimizations that improve the time to build up a program and update it based on file changes, especially in `--watch` mode or editor scenarios.[4] This includes avoiding array allocations during path normalization.[4]
*   **Import Assertions:**  `--module nodenext` in TypeScript 5.8 will issue an error if it encounters an import assertion, as Node.js 22 no longer accepts them using the `assert` syntax, recommending `with` instead.[4]

**Other Notable Features & Improvements:**

*   **Inferred Type Predicates:** Improved type inference, especially with arrays and filtering.[3]
*   **Control Flow Narrowing for Constant Indexed Accesses:** Better type narrowing for accessing object properties.[3]
*   **Regular Expression Syntax Checking:** Basic syntax checks are performed on regular expressions, flagging errors like unclosed parentheses.[3]
*   **Array filter Fixes:** Properly filters the type of arrays when you use the filter function.
*   **Object Key Inference Fixes:** Improves type inference.

**Performance Enhancements:**

*   **Go Rewrite:**  A full rewrite of TypeScript in Go has been promised for version 7.0, which has demonstrated significant speed improvements (up to 10x-15x in some cases).[5] This will affect the compiler (`tsc`) and IDE performance (loading, hovers, errors, etc.).[5] The team chose Go for its structural similarity to the current JavaScript implementation.[5]
*   **TypeScript 5.0:** This update aimed to accelerate coding processes and simplify development by refining code, data structures, and streamlining import/export operations.[6]

In summary, TypeScript is continuously evolving with new features and improvements aimed at enhancing developer productivity, code quality, and performance.[6]

Sources:
[1] edicomgroup.com (https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFILdgh_4-Yh0OuwzDOqwCfvLGHdhm_PGhdAIzMK_DFwW38X9qK8b3Tj_ws2VZ2VLxWW_NJtuzot8B_wYYH4rOHBY_1HYZ7PyCHOCR3GzQpwQUi71ufAf6izU13O3W6GzjQAQnVjnheeRLLLf4mD7uueIS-g0yeivFo2XWZKJF4wtRtDfdTYjtHvRYmB7rY6Q==)
[2] dev.to (https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFFMJOcmJDu8TUJsc6cKjVMDTR7ggjQMUc1aMAIVKRhbTq7Zjzh5f_h-UpZn6LE6xB-nTqUmQwHCiUmhvAZ_uYmzXIzNmJvtoDUjDcB9hJDw_aPPvJjd411APwVfiNvd3yhlrB7MFsnxH25-hxNetmoZJrriZ0mGm6ZaYbm0yMeiruDqC5mnqXJwuyGLMdrg-M3LpRAGrxVAT9b1veE)
[3] dev.to (https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFfDcb-2QNwLZ0TpjSkNCWCvh-dvslYtllEMyyTXCSu-3jbOBD4vvq0j5Hqyuw8BcmEpKjBBeBZS83E-GCKax48hg5Oc1Fam6GQy296DxQkEQOfg7pvmnRhE3tdDbDCBqXKdYPonoR_AVLBAlGdKg==)
[4] microsoft.com (https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEWKA9uZsB7lcOGcnOLveyjImsqVwNItCj3n3QiCrCkyL6iY4rA16Wp37FecAoKgX58lcDcBOuXye97fgw5SAbLwDkl3M-vCUK0I0HxtCx8qMaBVM42sxyFEQjn1iz4Qgzud3P7pDlc4frHf6Wkgs8nNcoIlMriePVOb0l9vmY=)
[5] totaltypescript.com (https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFysE6zFlg_XiXfGqAiDapTIj2bsVWlkuq3Trpfacjd1a7gMDrUh35MKW-No9qdSKti68W3M2b1j6VqlnZ7v_yBOjE8hK_3d57U7UePyjMOUDdbBBGRK8CZeUug3hBOFsZjbnQoDdoL446oZL1R38gJrc9JvGmlWQno)
[6] rabitsolutions.com (https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEY1brKmgxI5YOA1HrB89SnHNPyhm3Dlz-zumJMoi-wBegLSOjto360JJrA29TwVB8A02qHWZBtwua0QHn8NxAjWUCCkLxD7lZa_xW4Mtp8diiAXl1ppIWEHq6T7B1Mm6_dMs3lWoOKOJSjCUrk6-P4ao40V-nYULfPtA==)
</details>

Development

To contribute to this project:

# Clone the repository
git clone https://github.com/yukukotani/mcp-gemini-google-search.git
cd mcp-gemini-google-search

# Install dependencies
npm install

# Development mode (watch for file changes)
npm run dev

# Build
npm run build

# Run locally
npm run start

# Debug with MCP Inspector
npm run inspect

Debugging with MCP Inspector

Running npm run inspect will open the MCP Inspector in your browser. This allows you to:

  • View available tools
  • Execute tools and see responses
  • Debug in real-time

License

Apache License 2.0

FAQ

What is the Google Search (Gemini) MCP server?
Google Search (Gemini) is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Google Search (Gemini)?
This profile displays 34 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Web Research & Information Gathering

Fetch and extract information from websites automatically

Example

Research competitor pricing, scrape product reviews, monitor news mentions

Automate 5-10 hours/week of manual web research

Content Monitoring & Alerts

Track website changes, new content, price updates

Example

Monitor competitor blog for new posts, track stock availability, watch for pricing changes

Stay informed without manual checking, never miss important updates

Data Extraction & Aggregation

Extract structured data from multiple websites

Example

Compile product listings from 10 e-commerce sites, aggregate job postings, collect real estate data

Build datasets 100x faster than manual copying

API-less Integration

Interact with services that don't offer APIs

Example

Check form submissions, validate website functionality, test user flows

Automate interactions with any website, even without API

Implementation Guide

Prerequisites

  • Claude Desktop or Cursor with MCP support
  • Understanding of web scraping ethics and robots.txt
  • Rate limiting awareness to avoid overwhelming target sites
  • Knowledge of legal restrictions on data collection

Time Estimate

20-40 minutes including configuration and testing

Installation Steps

  1. 1.Install web automation MCP server via npm or pip
  2. 2.Configure allowed domains and rate limits in MCP config
  3. 3.Test with simple fetch: 'Get content from example.com'
  4. 4.Progress to extraction: 'Extract all product prices from this page'
  5. 5.Set up monitoring: 'Check this URL daily for changes'
  6. 6.Parse structured data: 'Create CSV from this table'
  7. 7.Respect robots.txt and rate limits always

Troubleshooting

  • 403 Forbidden: Website blocks bots—respect their wishes, use official API instead
  • Rate limit errors: Slow down requests, add delays between fetches
  • Stale data: Target site changed HTML structure—update selectors
  • Timeout errors: Site is slow or blocking—increase timeout, try different user agent
  • JavaScript-rendered content: Use headless browser MCP servers for dynamic sites

Best Practices

✓ Do

  • +Check robots.txt and respect crawl rules
  • +Rate limit requests: 1-2 requests/second maximum
  • +Use official APIs when available instead of scraping
  • +Identify your bot with descriptive user agent
  • +Cache results to minimize repeated requests
  • +Handle errors gracefully with retries and fallbacks
  • +Validate extracted data for accuracy

✗ Don't

  • Don't scrape sites that explicitly forbid it (robots.txt, ToS)
  • Don't overwhelm servers with rapid requests—use rate limiting
  • Don't scrape personal data without consent and legal basis
  • Don't ignore copyright on extracted content
  • Don't assume HTML structure is stable—handle changes
  • Don't use scraped data for commercial purposes without permission

💡 Pro Tips

  • Use CSS selectors or XPath for robust data extraction
  • Set up monitoring alerts for extraction failures (structure changed)
  • Implement exponential backoff for retries on failures
  • Store raw HTML for reprocessing if extraction logic changes
  • Combine with data analysis tools for insights from extracted data
  • Consider using official APIs or RSS feeds as more stable alternatives

Technical Details

Architecture

MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.

Protocols

  • HTTP/HTTPS
  • WebSocket (for real-time sites)
  • Puppeteer/Playwright (for JavaScript sites)

Compatibility

  • Static HTML sites
  • JavaScript-rendered SPAs (with headless browser)
  • REST APIs
  • GraphQL endpoints

When to Use This

✓ Use When

Use for research automation, content monitoring, data aggregation from multiple sources, and when official APIs don't exist. Best for read-only information gathering.

✗ Avoid When

Avoid for sites with APIs (use API instead), sites that explicitly forbid scraping, when data is copyrighted, or for login-required content without proper authorization.

Integration

  • Scheduled monitoring with change detection
  • Multi-source data aggregation pipelines
  • Fallback to web scraping when API rate limits hit
  • Headless browser for JavaScript-heavy sites

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.734 reviews
  • Maya Martin· Dec 24, 2024

    Google Search (Gemini) is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Camila Smith· Dec 12, 2024

    We wired Google Search (Gemini) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Sakshi Patil· Nov 27, 2024

    Useful MCP listing: Google Search (Gemini) is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Maya Yang· Nov 15, 2024

    Google Search (Gemini) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Amina Jain· Nov 11, 2024

    Useful MCP listing: Google Search (Gemini) is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Maya Patel· Nov 3, 2024

    We evaluated Google Search (Gemini) against two servers with overlapping tools; this profile had the clearer scope statement.

  • Yusuf Ramirez· Oct 22, 2024

    Google Search (Gemini) is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Chaitanya Patil· Oct 18, 2024

    Google Search (Gemini) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Maya Kapoor· Oct 6, 2024

    We wired Google Search (Gemini) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Amina Diallo· Oct 2, 2024

    Google Search (Gemini) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

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