A Model Context Protocol server providing Google AI-powered search and documentation tools for developers
A Model Context Protocol server providing Google AI-powered search and documentation tools for developers
This project implements a Model Context Protocol (MCP) server that provides a comprehensive suite of Google AI-powered search and documentation tools specifically designed to help AI coders overcome LLM knowledge gaps and information limitations.
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 via gcloud auth application-default login is recommended, or a Service Account Key) OR Gemini API key.Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| 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 | Enhanced technology comparison with quantitative benchmarks, performance metrics, market adoption statistics, and detailed evidence-based analysis across multiple criteria. |
| architecture_pattern_recommendation | Comprehensive architecture guidance with performance metrics, quantitative benefits, detailed implementation roadmaps, and evidence-based pattern recommendations for specific use cases. |
{
"mcpServers": {
"google-ai-search": {
"command": "npx",
"args": ["-y", "bun"],
"env": {
"AI_PROVIDER": "your-value",
"GOOGLE_CLOUD_PROJECT": "your-value",
"GEMINI_API_KEY": "your-value",
"VERTEX_MODEL_ID": "your-value",
"GEMINI_MODEL_ID": "your-value",
"GOOGLE_CLOUD_LOCATION": "your-value",
"AI_TEMPERATURE": "your-value",
"AI_USE_STREAMING": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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 via gcloud auth application-default login is recommended, or a Service Account Key) OR Gemini API key.| Variable | Description | Required |
|---|---|---|
| AI_PROVIDER | Configuration value read at startup. | Optional |
| GOOGLE_CLOUD_PROJECT | Configuration value read at startup. | Optional |
| GEMINI_API_KEY | Credential the server authenticates with. | Yes |
| VERTEX_MODEL_ID | Configuration value read at startup. | Optional |
| GEMINI_MODEL_ID | Configuration value read at startup. | Optional |
| GOOGLE_CLOUD_LOCATION | Configuration value read at startup. | Optional |
| AI_TEMPERATURE | Configuration value read at startup. | Optional |
| AI_USE_STREAMING | Configuration value read at startup. | Optional |
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