Gemini AI MCP SERVER

🚀 Gemini AI MCP Server The Gemini AI MCP Server integrates Google’s Gemini language models with Apify’s automation platform using the Model Context Protocol (MCP). This actor acts as a bridge between scraping workflows and AI analysis, allowing real-time natural-language understanding, data summarization, classification, and structured extraction within your Apify or AI pipelines. ## 🧠 Key Features ✅ Gemini API integration Seamlessly connects with Google’s Gemini models (gemini-2.5-flash, etc.) using your API key. ✅ Real-time AI data processing Analyze and interpret scraped datasets as soon as they’re collected — directly in your Apify workflows. ✅ Flexible prompt customization Define tasks such as summarization, sentiment analysis, categorization, or structured extraction using natural language. ✅ Apify workflow integration Run any Apify Actor (e.g., bhansalisoft~google-map-business-scraper) and automatically analyze its dataset output using Gemini AI. ✅ Secure key storage Users can safely add, update, or verify their Gemini API key using interactive MCP tools. ✅ Structured output for automation Return JSON-formatted results suitable for further processing or database insertion. ## 🧩 Tools Exposed by MCP Server | Tool name | Description | | ------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | save_gemini_api_key(api_key) | Saves the user-provided Gemini API key locally for persistent sessions. | | gemini_status | Checks if the saved API key is valid and the model responds correctly. | | scrape_and_analyze(apify_actor_id, input_json, task_prompt) | Runs an Apify scraper actor, fetches dataset results, and analyzes them via Gemini AI. | | gemini_analyze_text(task, text) | Analyze raw text with Gemini (summarization, sentiment, etc.). | | gemini_analyze_url(url, task) | Fetches a web page, extracts text, and performs Gemini analysis. | | gemini_categorize(labels_json, text) | Categorizes text into provided labels and returns label + confidence. | | gemini_structured_extract(text, schema_json) | Extracts structured data based on a given JSON schema. | | gemini_embed_text(text) | Returns an embedding vector for semantic search or clustering. | | gemini_set_defaults(model, temperature) | Updates the default Gemini model and temperature used by the server. | | summarize_scraped_data(json_data) | Summarizes pre-scraped JSON data using Gemini AI. | ## 🔍 MCP Connection Configuration (Claude Desktop / LangGraph) MCP SERVER URL Will be https://bhansalisoft--gemini-mcp-server.apify.actor/mcp Add to your claude_desktop_config.json: json { "mcpServers": { "gemini-mcp-server": { "command": "npx", "args": [ "mcp-remote", "https://bhansalisoft--gemini-mcp-server.apify.actor/mcp?token=[Your APIFY KEY]" ] } } } After connection, all Gemini tools will appear automatically in your MCP client. ## Demo Videos Check Demo video using TEST MCP Client https://youtu.be/VDy2kqkYqxU Check Demo using MCP inspector Tools https://youtu.be/EEyeP6VpyKg