| """
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| Event Orchestrator MCP Server
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| Provides event discovery tools through Model Context Protocol (MCP).
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| Coordinates multiple MCP event discovery tools in parallel.
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| Wraps the EventOrchestrator to expose it as an MCP server with tools for:
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| - find_events: Search for events using all available tools
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| - get_event_details: Get detailed information about an event
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| """
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|
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| import os
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| import logging
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| from typing import Any, Optional
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| import asyncio
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|
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| from fastmcp import FastMCP
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| from pydantic import BaseModel
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| import httpx
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| from event_orchestrator import EventOrchestrator
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| logging.basicConfig(
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| level=logging.INFO,
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| format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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| )
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| logger = logging.getLogger(__name__)
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| class FindEventsInput(BaseModel):
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| """Input for find_events tool"""
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| query: str
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| city: str
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| country: str = "USA"
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| date_range: str = ""
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| interests: list = []
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| llm_provider: str = "google"
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|
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| class EventResult(BaseModel):
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| """Event result from orchestrator"""
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| name: str
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| date: str
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| location: str
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| description: str
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| url: str = ""
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| source: str
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| price: str = ""
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| confidence: float
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| booking_url: str = ""
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| def make_mcp_server() -> FastMCP:
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| """Create and configure the Event Orchestrator MCP server"""
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| mcp = FastMCP("event-orchestrator-mcp")
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| orchestrator_instance = None
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| @mcp.tool()
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| async def find_events(
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| query: str,
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| city: str,
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| country: str = "USA",
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| date_range: str = "",
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| interests: Optional[list] = None,
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| llm_provider: str = "google"
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| ) -> dict:
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| """
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| Find events using coordinated multi-tool discovery.
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|
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| Searches for events in the specified location using:
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| - Web Search (Brave)
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| - Gemini Search (AI-based)
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| - Eventbrite (Event platform)
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| - Ticketmaster (Ticketing platform)
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| - Ultimate Event Scraper (Web crawler)
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| - Jina AI (Content extraction)
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|
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| Args:
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| query: Main search query (e.g., "rock concerts", "tech conferences")
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| city: Target city for events
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| country: Target country (default: USA)
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| date_range: Event date range filter (e.g., "2025-01")
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| interests: List of user interests for ranking (e.g., ["music", "live"])
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| llm_provider: LLM provider for tool selection (google, openai, anthropic)
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|
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| Returns:
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| Dictionary with:
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| - status: "success" or "error"
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| - events: List of top 5 events
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| - total_found: Total events discovered
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| - tools_used: List of tools that returned results
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| - reasoning: Why these tools were selected
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| """
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| nonlocal orchestrator_instance
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| try:
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| if not orchestrator_instance:
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| gateway_url = os.getenv("GATEWAY_URL")
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| if not gateway_url:
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| logger.error("❌ GATEWAY_URL not set in Modal secrets (gateway-secret)")
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| raise ValueError("GATEWAY_URL must be set in Modal secrets. Run: modal secret create gateway-secret GATEWAY_URL=<url>")
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| orchestrator_instance = EventOrchestrator(
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| gateway_url=gateway_url,
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| llm_client=None
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| )
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| logger.info(f"🎯 Orchestrator initialized with gateway: {gateway_url}")
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|
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| if interests is None:
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| interests = []
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| if not date_range:
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| date_range = ""
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| logger.info(f"🔍 Finding events: '{query}' in {city}, {country}")
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| result = await orchestrator_instance.find_events(
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| query=query,
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| city=city,
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| country=country,
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| date_range=date_range,
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| interests=interests,
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| llm_provider=llm_provider
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| )
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| response = {
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| "status": "success",
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| "query": query,
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| "location": f"{city}, {country}",
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| "events": result.get("events", []),
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| "total_found": result.get("total_found", 0),
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| "tools_used": result.get("tools_used", []),
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| "reasoning": result.get("reasoning", ""),
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| "execution_time": result.get("execution_time", 0)
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| }
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| logger.info(f"✅ Found {len(result.get('events', []))} events")
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| return response
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|
|
| except Exception as e:
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| logger.error(f"Error finding events: {e}", exc_info=True)
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| return {
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| "status": "error",
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| "error": str(e),
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| "events": [],
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| "total_found": 0,
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| "tools_used": [],
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| "query": query,
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| "location": f"{city}, {country}"
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| }
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| @mcp.tool()
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| async def get_orchestrator_status() -> dict:
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| """
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| Get the current status of the Event Orchestrator.
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| Returns information about:
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| - Available tools and their status
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| - Gateway connectivity
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| - API key configuration
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| - Performance metrics
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| """
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| try:
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| gateway_url = os.getenv("GATEWAY_URL")
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| jina_key = os.getenv("JINA_API_KEY", "")
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| google_key = os.getenv("GOOGLE_API_KEY", "")
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| openai_key = os.getenv("OPENAI_API_KEY", "")
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| anthropic_key = os.getenv("ANTHROPIC_API_KEY", "")
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| brave_key = os.getenv("BRAVE_API_KEY", "")
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|
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| if not gateway_url:
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| gateway_url = "NOT_CONFIGURED"
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| gateway_up = False
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| try:
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| async with httpx.AsyncClient(timeout=5.0) as client:
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| response = await client.get(f"{gateway_url}/health")
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| gateway_up = response.status_code == 200
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| except:
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| gateway_up = False
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|
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| return {
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| "status": "operational",
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| "gateway": {
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| "url": gateway_url,
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| "up": gateway_up
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| },
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| "tools": [
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| "web-search (Brave)",
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| "gemini-search (Google)",
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| "eventbrite",
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| "ticketmaster",
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| "ultimate_event_scraper",
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| "jina-ai-mcp"
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| ],
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| "api_keys": {
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| "jina": "✓" if jina_key else "✗",
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| "google": "✓" if google_key else "✗",
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| "openai": "✓" if openai_key else "✗",
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| "anthropic": "✓" if anthropic_key else "✗",
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| "brave": "✓" if brave_key else "✗"
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| }
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| }
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|
|
| except Exception as e:
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| logger.error(f"Error getting status: {e}")
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| return {
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| "status": "error",
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| "error": str(e)
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| }
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|
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| @mcp.tool()
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| async def get_available_tools() -> dict:
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| """
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| Get information about all available event discovery tools.
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|
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| Returns details about:
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| - Tool names and capabilities
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| - Supported parameters
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| - Known limitations
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| """
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| return {
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| "tools": [
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| {
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| "name": "web-search",
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| "provider": "Brave",
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| "type": "General web search",
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| "capabilities": ["search", "ranking"],
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| "requires_api_key": True
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| },
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| {
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| "name": "gemini-search",
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| "provider": "Google Gemini AI",
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| "type": "AI-based semantic search",
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| "capabilities": ["semantic_search", "analysis"],
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| "requires_api_key": True
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| },
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| {
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| "name": "eventbrite",
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| "provider": "Eventbrite Platform",
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| "type": "Event platform scraper",
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| "capabilities": ["event_discovery", "ticketing"],
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| "requires_api_key": False
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| },
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| {
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| "name": "ticketmaster",
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| "provider": "Ticketmaster Platform",
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| "type": "Ticketing platform scraper",
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| "capabilities": ["event_discovery", "ticketing"],
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| "requires_api_key": False
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| },
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| {
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| "name": "ultimate_event_scraper",
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| "provider": "Custom Web Scraper",
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| "type": "Advanced web crawler with Playwright",
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| "capabilities": ["web_scraping", "js_rendering"],
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| "requires_api_key": False
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| },
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| {
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| "name": "jina-ai-mcp",
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| "provider": "Jina AI",
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| "type": "Content extraction and ranking",
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| "capabilities": ["web_scraping", "ranking", "extraction"],
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| "requires_api_key": True
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| }
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| ],
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| "llm_providers": ["google", "openai", "anthropic"],
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| "max_events_per_search": 5,
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| "supported_countries": ["USA", "Australia", "UK", "Canada"]
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| }
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|
|
|
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| logger.info("✅ Event Orchestrator MCP Server initialized with 3 tools")
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|
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| return mcp
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|
|
|
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|
|
|
|
|
| if __name__ == "__main__":
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| import uvicorn
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|
|
| mcp = make_mcp_server()
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|
|
|
|
| uvicorn.run(
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| mcp.http_app(
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| transport="streamable-http",
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| stateless_http=True,
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| ),
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| host="0.0.0.0",
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| port=8000,
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| log_level="info"
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| )
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|
|
