Google Search (Gemini)▌
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.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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
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.Install web automation MCP server via npm or pip
- 2.Configure allowed domains and rate limits in MCP config
- 3.Test with simple fetch: 'Get content from example.com'
- 4.Progress to extraction: 'Extract all product prices from this page'
- 5.Set up monitoring: 'Check this URL daily for changes'
- 6.Parse structured data: 'Create CSV from this table'
- 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
4.7★★★★★34 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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