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.

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: 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.

(Note: Input/output schemas for each tool are defined in their respective files within

Prerequisites

• 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 via gcloud auth application-default login is recommended, or a Service Account Key) OR Gemini API key.

Setup & Installation

1. Clone/Place Project: Ensure the project files are in your desired location.

2. Install Dependencies:

   bun install

3. Configure Environment:

   • Create a .env file in the project root (copy .env.example).

   • Set the required and optional environment variables as described in .env.example.

     • Set AI_PROVIDER to either "vertex" or "gemini".

     • If AI_PROVIDER="vertex", GOOGLE_CLOUD_PROJECT is required.

     • If AI_PROVIDER="gemini", GEMINI_API_KEY is required.

4. Build the Server:

   bun run build

   This compiles the TypeScript code to build/index.js.

Usage (Standalone / NPX)

Once published to npm, you can run this server directly using npx:

Alternatively, install it globally:

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

Running with Cline

1. 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 uses node to run the compiled script directly. It's useful during development when you have the code cloned locally.

   { "mcpServers": { "google-ai-search-mcp": { "command": "node", "args": [ "/full/path/to/your/google-ai-search-mcp/build/index.js" // Use absolute path or ensure it's relative to where Cline runs node ], "env": { // --- General AI Configuration --- "AI_PROVIDER": "vertex", // "vertex" or "gemini" // --- Required (Conditional) --- "GOOGLE_CLOUD_PROJECT": "YOUR_GCP_PROJECT_ID", // Required if AI_PROVIDER="vertex" // "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY", // Required if AI_PROVIDER="gemini" // --- Optional Model Selection --- "VERTEX_MODEL_ID": "gemini-2.5-pro", // If AI_PROVIDER="vertex" (Example override) "GEMINI_MODEL_ID": "gemini-2.5-pro", // If AI_PROVIDER="gemini" // --- Optional AI Parameters --- "GOOGLE_CLOUD_LOCATION": "us-central1", // Specific to Vertex AI "AI_TEMPERATURE": "0.0", "AI_USE_STREAMING": "true", "AI_MAX_OUTPUT_TOKENS": "65536", // Default from .env.example "AI_MAX_RETRIES": "3", "AI_RETRY_DELAY_MS": "1000", // --- Optional Vertex Authentication --- // "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/your/service-account-key.json" // If using Service Account Key for Vertex }, "disabled": false, "alwaysAllow": [ // Add tool names here if you don't want confirmation prompts // e.g., "answer_query_websearch" ], "timeout": 3600 // Optional: Timeout in seconds } // Add other servers here... } }

   • Important: Ensure the args path points correctly to the build/index.js file. Using an absolute path might be more reliable.

   Option B: Using NPX (Requires Package Published to npm)

   This method uses npx to automatically download and run the server package from the npm registry. This is convenient if you don't want to clone the repository.

   { "mcpServers": { "google-ai-search-mcp": { "command": "bunx", // Use bunx "args": [ "-y", // Auto-confirm installation "google-ai-search-mcp" // The npm package name ], "env": { // --- General AI Configuration --- "AI_PROVIDER": "vertex", // "vertex" or "gemini" // --- Required (Conditional) --- "GOOGLE_CLOUD_PROJECT": "YOUR_GCP_PROJECT_ID", // Required if AI_PROVIDER="vertex" // "GEMINI_API_KEY": "YOUR_GEMINI_API_KEY", // Required if AI_PROVIDER="gemini" // --- Optional Model Selection --- "VERTEX_MODEL_ID": "gemini-2.5-pro", // If AI_PROVIDER="vertex" (Example override) "GEMINI_MODEL_ID": "gemini-2.5-pro", // If AI_PROVIDER="gemini" // --- Optional AI Parameters --- "GOOGLE_CLOUD_LOCATION": "us-central1", // Specific to Vertex AI "AI_TEMPERATURE": "0.0", "AI_USE_STREAMING": "true", "AI_MAX_OUTPUT_TOKENS": "65536", // Default from .env.example "AI_MAX_RETRIES": "3", "AI_RETRY_DELAY_MS": "1000", // --- Optional Vertex Authentication --- // "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/your/service-account-key.json" // If using Service Account Key for Vertex }, "disabled": false, "alwaysAllow": [ // Add tool names here if you don't want confirmation prompts // e.g., "answer_query_websearch" ], "timeout": 3600 // Optional: Timeout in seconds } // Add other servers here... } }

   • Ensure the environment variables in the env block are correctly set, either matching .env or explicitly defined here. Remove comments from the actual JSON file.

2. Restart/Reload Cline: Cline should detect the configuration change and start the server.

3. Use Tools: You can now use the comprehensive list of Google AI-powered search and documentation tools via Cline.

Development

• Watch Mode: bun run watch

• Build: bun run build

• Inspector: bun run inspector

License

This project is licensed under the MIT License - see the LICENSE file for details.