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 grounding capabilities with automatic source citations and metadata for reliable information retrieval.
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.
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.
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.
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
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
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
Share your MCP server with the developer community
Google Search (Gemini) is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We wired Google Search (Gemini) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Useful MCP listing: Google Search (Gemini) is the kind of server we cite when onboarding engineers to host + tool permissions.
Google Search (Gemini) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Google Search (Gemini) is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated Google Search (Gemini) against two servers with overlapping tools; this profile had the clearer scope statement.
Google Search (Gemini) is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Google Search (Gemini) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired Google Search (Gemini) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Google Search (Gemini) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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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.
npm install -g mcp-gemini-google-search
# 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)
You can set up this MCP server in Claude Code using the CLI:
# 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
# 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
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
Search Google for information.
Parameters:
query (string, required): Search queryExample:
latest TypeScript features
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==)
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
Running npm run inspect will open the MCP Inspector in your browser. This allows you to:
Apache License 2.0
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
Prerequisites
Time Estimate
20-40 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.
Protocols
Compatibility
✓ 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.