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AI-Powered Local Event Finder with Multi-Tool Search

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Created by: Jez || jez

Jez

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Last update 8 days ago

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Summary

This n8n workflow implements an AI-powered "Local Event Finder" agent. It takes user criteria (like event type, city, date, and interests), uses a suite of search tools (Brave Web Search, Brave Local Search, Google Gemini Search) and a web scraper (Jina AI) to find relevant events, and returns formatted details. The entire agent is exposed as a single, easy-to-use MCP (Multi-Capability Peer) tool, making it simple to integrate into other workflows or applications.

This template cleverly combines the MCP server endpoint and the AI agent logic into a single n8n workflow file for ease of import and management.

Key Features

  • Intelligent Multi-Tool Search: Dynamically utilizes web search, precise local search, and advanced Gemini semantic search to find events.
  • Detailed Information via Web Scraping: Employs Jina AI to extract comprehensive details directly from event web pages.
  • Simplified MCP Tool Exposure: Makes the complex event-finding logic available as a single, callable tool for other MCP-compatible clients (e.g., Roo Code, Cline, other n8n workflows).
  • Customizable AI Behavior: The core AI agent's behavior, tool usage strategy, and output formatting can be tailored by modifying its System Prompt.
  • Modular Design: Uses distinct nodes for LLM, memory, and each external tool, allowing for easier modification or extension.

Benefits

  • Simplifies Client-Side Integration: Offloads the complexity of event searching and data extraction from client applications.
  • Provides Richer Event Data: Goes beyond simple search links to extract and format key event details.
  • Flexible & Adaptable: Can be adjusted to various event search needs and can incorporate new tools or data sources.
  • Efficient Processing: Leverages specialized tools for different aspects of the search process.

Nodes Used

  • MCP Trigger
  • Tool Workflow
  • Execute Workflow Trigger
  • AI Agent
  • Google Gemini Chat Model (ChatGoogleGenerativeAI)
  • Simple Memory (Window Buffer Memory)
  • MCP Client (for Brave Search tools via Smithery)
  • Google Gemini Search Tool
  • Jina AI Tool

Prerequisites

  • An active n8n instance.
  • Google AI API Key: For the Gemini LLM (Google Gemini Chat Model node) and the Google Gemini Search Tool. Ensure your key is enabled for these services.
  • Jina AI API Key: For the jina_ai_web_page_scraper node. A free tier is often available.
  • Access to a Brave Search MCP Provider (Optional but Recommended):
    • This template uses MCP Client nodes configured for Brave Search via a provider like Smithery. You'll need an account/API key for your chosen Brave Search MCP provider to configure the smithery brave search credential.
    • Alternatively, you could adapt these to call Brave Search API directly if you manage your own access, or replace them with other search tools.

Setup Instructions

  1. Import Workflow: Download the JSON file for this template and import it into your n8n instance.
  2. Configure Credentials:
    • Google Gemini LLM:
      • Locate the Google Gemini Chat Model node.
      • Select or create a "Google Gemini API" credential (named Google Gemini Context7 in the template) using your Google AI API Key.
    • Google Gemini Search Tool:
      • Locate the google_gemini_event_search node.
      • Select or create a "Gemini API" credential (named Gemini Credentials account in the template) using your Google AI API Key (ensure it's enabled for Search/Vertex AI).
    • Jina AI Web Scraper:
      • Locate the jina_ai_web_page_scraper node.
      • Select or create a "Jina AI API" credential (named Jina AI account in the template) using your Jina AI API Key.
    • Brave Search (via MCP):
      • You'll need an MCP Client HTTP API credential to connect to your Brave Search MCP provider (e.g., Smithery).
      • Create a new "MCP Client HTTP API" credential in n8n. Name it, for example, smithery brave search.
      • Configure it with the Base URL and any required authentication (e.g., API key in headers) for your Brave Search MCP provider.
      • Locate the brave_web_search and brave_local_search MCP Client nodes in the workflow.
      • Assign the smithery brave search (or your named credential) to both of these nodes.
  3. Activate Workflow: Ensure the workflow is active.
  4. Note MCP Trigger Path:
    • Locate the local_event_finder (MCP Trigger) node.
    • The Path field (e.g., 0ca88864-ec0a-4c27-a7ec-e28c5a900697) combined with your n8n webhook base URL forms the endpoint for client calls.
    • Example Endpoint: YOUR_N8N_INSTANCE_URL/webhooks/PATH-TO-MCP-SERVER

Customization

  • AI Behavior: Modify the "System Message" parameter within the event_finder_agent node to change the AI's persona, its strategy for using tools, or the desired output format.
  • LLM Model: Swap the Google Gemini Chat Model node with another compatible LLM node (e.g., OpenAI Chat Model) if desired. You'll need to adjust credentials and potentially the system prompt.
  • Tools: Add, remove, or replace tool nodes (e.g., use a different search provider, add a weather API tool) and update the event_finder_agent's system prompt and tool configuration accordingly.
  • Scraping Depth: Be mindful of the jina_ai_web_page_scraper's usage due to potential timeouts. The system prompt already guides the LLM on this, but you can adjust its usage instructions.