shariqriazz/google-ai-search-mcp

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.
Implementation notes
Provider selection and credentials are resolved at runtime, so a tool being listed does not prove that its upstream provider is configured or reachable. Treat model-produced comparisons, architecture guidance, and security analysis as material to verify against the cited primary sources rather than deterministic findings.
For a source-linked comparison of the design pressures across this project and six other public MCP implementations, see What building seven MCP servers taught me about production MCP.
Features
* 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).
Tools Provided
Core Search & Documentation Tools
* 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.
Advanced Analysis Tools
* 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 within src/tools/ and exposed via the MCP server.)*
Prerequisites
* Node.js (v18+)
* Bun (npm install -g bun)
* Google Cloud Project with Billing enabled (if using Vertex AI).