An AI agent that autonomously searches multiple sources, scrapes rich event data, and returns structured details for integration.
It receives user criteria (event type, city, date, and interests), queries multiple sources, and consolidates results. It scrapes event pages to extract details like time, venue, price, and accessibility. It outputs structured data ready to feed into downstream workflows or apps and can be reconfigured for different output formats.
Orchestrates searches, extracts rich details, and formats results for integration.
Ingest criteria from user requests.
Coordinate local and web searches across multiple sources.
Query sources, correlate results, and rank relevance.
Scrape pages to extract dates, times, venues, and prices.
Format results into structured payloads for MCP clients.
Return results to the calling workflow or application.
This AI agent replaces fragmented manual work with a predictable execution flow.
A simple, three-step flow that non-technical users can follow.
The AI agent receives user criteria (event type, city, date range, interests) via MCP and builds a targeted search plan.
The AI agent coordinates Brave Local Search, Brave Web Search, and Google Gemini Search to collect candidate events.
The AI agent uses Jina AI to scrape pages and generates a structured output for integration.
A realistic scenario showing end-to-end operation and outcome.
Scenario: A city guide app requests jazz events in Seattle for the upcoming Saturday under $40. The AI agent ingests the criteria, performs multi-tool searches, scrapes key details from event pages, and returns a structured list of 6 events with name, date, time, venue, price, and a link for each.
Roles that benefit from automated local event discovery and structured data.
Need reliable data feeds and structured outputs to power in-app search and recommendations.
Require up-to-date event details with consistent formatting for UI components.
Benefit from aggregated event data with rich descriptions and pricing.
Can automate event listings with accurate metadata for readers.
Gain exposure by feeding event details into partner apps and catalogs.
Use structured event data to drive targeted campaigns and promotions.
Tools wired into the AI agent to perform search, scraping, and orchestration.
Performs broad web searches to identify relevant event pages.
Executes precise local queries for city and venue-specific results.
LLM to interpret criteria and reason about results.
Semantic search to enrich candidate events with context.
Scrapes pages to extract structured event details (date, time, venue, price).
Connects MCP-enabled sources to acquire results and route payloads.
Concrete scenarios where the AI agent adds value.
Common questions about deploying and using the AI agent.
It is an AI-powered agent that orchestrates multiple search tools and a scraping capability to locate local events, extract detailed data, and deliver a structured payload suitable for integration. It runs end-to-end, from user criteria intake to a ready-to-use data feed. The agent supports configurable prompts and tool usage strategies to align with your needs. Outputs are designed for seamless consumption by downstream workflows and apps.
The agent queries real-time sources and validates core attributes (date, time, venue) against multiple sources when possible. It uses a scraping step to capture current page data and formats it into a consistent schema. If discrepancies arise, it can flag uncertain results for manual review. The system prompt guides the agent to prioritize reliability and provenance. Regular credential updates help maintain access to current data sources.
The agent uses a combination of Brave Web Search, Brave Local Search, and Google Gemini Search to gather candidate events. It also uses Jina AI for scraping to enrich data with detailed attributes. The orchestration layer coordinates these tools to balance breadth and depth of results. Outputs are normalized into a single, structured payload for downstream use.
Credentials are configured inside the agent's orchestration workflow. You will set up access to the Gemini API, Brave MCP providers, and Jina AI via the respective credential objects. The setup includes assigning credentials to each tool node and validating that permissions cover both search and scraping capabilities. After configuration, you can activate the agent for endpoint-based calls via MCP triggers.
Yes. The agent's system prompt and output formatting rules can be adjusted to match your downstream schema. You can modify the fields included (e.g., event name, date, time, venue, price, URL) and how they are serialized for the receiving app. The agent is designed to output a consistent, structured payload suitable for MCP clients and standard integrations. Changes can be applied without altering the core orchestration logic.
The agent prioritizes data from multiple sources and can assign confidence scores to each item. If essential fields are missing, it may attempt a secondary fetch or mark the item for manual review. You can configure fallback behavior to skip unreliable entries or to include them with a caveat. This helps maintain overall data quality while avoiding downstream failures.
Yes. The agent is designed to integrate into MCP-enabled workflows and can be scaled to handle higher request throughput. It supports modular tool changes, allowing you to swap sources or add new scraping capabilities as needed. Monitoring and retries can be configured to ensure resilience. Consider provisioning sufficient API quotas and memory for peak loads.
An AI agent that autonomously searches multiple sources, scrapes rich event data, and returns structured details for integration.
