Google AI Search MCP

- Provides access to Google AI models (Vertex AI and Gemini API) via specialized MCP tools.
- Focuses on real-time information retrieval and documentation-based analysis.
- Supports web search grounding for current information that LLMs lack.
- Configurable model ID, temperature, streaming behavior, max output tokens, and retry settings via environment variables.
- Uses streaming API by default for potentially better responsiveness.
- Includes basic retry logic for transient API errors.
- Minimal safety filters applied (BLOCK_NONE) to reduce potential blocking (use with caution).

- answer_query_websearch: Developer-focused natural language queries with automatic technical detection, enhanced search methodology, and comprehensive code formatting using Google AI with real-time search results.
- explain_topic_with_docs: Streamlined technical explanations with improved debugging scenarios, synthesizing information from official documentation with reduced verbosity and enhanced troubleshooting guidance.
- get_doc_snippets: Enhanced code snippet retrieval with progressive complexity examples, advanced search patterns, version-specific targeting, and comprehensive context for technical queries from official documentation.
- generate_project_guidelines: Generates comprehensive structured project guidelines documents based on specified technologies, using web search for current best practices and industry standards.

- code_analysis_with_docs: Evidence-based code analysis with standardized citations, severity categorization, and actionable recommendations by comparing code against official documentation best practices.
- technical_comparison: Produces technology comparisons across requested criteria using current search context where available. Verify quantitative or market claims against the cited primary sources.
- architecture_pattern_recommendation: Produces architecture options, tradeoffs, and implementation considerations for a described use case. Validate the recommendation against the system's actual constraints before adopting it.

(Note: Input/output schemas for each tool are defined in their respective files withinsrc/tools/and exposed via the MCP server.)

- Node.js (v18+)
- Bun (npm install -g bun)
- Google Cloud Project with Billing enabled (if using Vertex AI).
- Vertex AI API enabled in the GCP project (if using Vertex AI).
- Google Cloud Authentication configured in your environment (Application Default Credentials viagcloud auth application-default loginis recommended, or a Service Account Key) OR Gemini API key.
- Clone/Place Project:Ensure the project files are in your desired location.
- Install Dependencies:

bun install

- Create a.envfile in the project root (copy.env.example).
- Set the required and optional environment variables as described in.env.example.

- SetAI_PROVIDERto either"vertex"or"gemini".
- IfAI_PROVIDER="vertex",GOOGLE_CLOUD_PROJECTis required.
- IfAI_PROVIDER="gemini",GEMINI_API_KEYis required.

The package is published to npm and can be run directly withnpx:

# Ensure required environment variables are set (e.g., GOOGLE_CLOUD_PROJECT or GEMINI_API_KEY) bunx google-ai-search-mcp

bun install -g google-ai-search-mcp # Then run: google-ai-search-mcp

Note:Running standalone requires setting necessary environment variables (likeGOOGLE_CLOUD_PROJECT,GOOGLE_CLOUD_LOCATION,GEMINI_API_KEY, authentication credentials if not using ADC) in your shell environment before executing the command.

docker run --rm -i \ -e AI_PROVIDER=gemini \ -e GEMINI_API_KEY \ google-ai-search-mcp

For Vertex AI, passAI_PROVIDER=vertex,GOOGLE_CLOUD_PROJECT, and optionallyGOOGLE_CLOUD_LOCATION. Application Default Credentials must also be available inside the container, normally through a read-only credential mount. Do not bake API keys or service-account files into the image.
-

Configure MCP Settings:Add/update the configuration in your Cline MCP settings file (e.g.,.roo/mcp.json). You have two primary ways to configure the command:

Option A: Using Node (Direct Path - Recommended for Development)

This method usesnodeto run the compiled script directly. It's useful during development when you have the code cloned locally.

Search global news using natural language. Webz.io News Search API returns the most relevant articles and content, with filters for source, country, language, date, sentiment, and category.

A connector for the Perplexity API to enable web search within the MCP ecosystem.

Structural AI Search Readiness MCP. Audit, dry-run fixes, rescore, doctor. No LLM rankings.

Perform web searches with the Brave Search API and analyze research papers using Google's Gemini model.

Provides AI-powered web search and summarization using the Gemini API's grounding feature.

Integrate search grounded Gemini output into your workflow.

Generates responses using the Gemini API and Google Search for up-to-date information.

Performs web searches using the Gemini Web Search Tool via the local gemini-cli.

Comprehensive web, news, and social media search and analysis using xAI's Grok API.

Enhances LLM responses with factual, real-time web content using Jina AI's grounding capabilities.