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d822f93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | from mcp.server.fastmcp import FastMCP
import random
import urllib.parse
mcp = FastMCP("DiningAgent")
# Destination-specific restaurants
DESTINATION_RESTAURANTS = {
"dubai": [
("At.mosphere", "World's highest restaurant at Burj Khalifa", "$$$$", "Fine Dining", 4.7),
("Pierchic", "Overwater seafood restaurant at Al Qasr", "$$$$", "Seafood", 4.6),
("Ossiano", "Underwater dining at Atlantis", "$$$$", "Seafood", 4.8),
("Al Hadheerah", "Arabian desert dining with live entertainment", "$$$", "Arabic", 4.5),
("Nusr-Et Steakhouse", "Salt Bae's famous steakhouse", "$$$$", "Steakhouse", 4.4),
("Arabian Tea House", "Traditional Emirati cuisine", "$$", "Local", 4.6),
("Ravi Restaurant", "Famous Pakistani street food", "$", "Pakistani", 4.5),
],
"paris": [
("Le Jules Verne", "Michelin-starred Eiffel Tower dining", "$$$$", "French", 4.5),
("Café de Flore", "Historic Left Bank café", "$$", "French Café", 4.3),
("L'Ambroisie", "3 Michelin star classic French", "$$$$", "Fine Dining", 4.9),
("Pink Mamma", "Trendy Italian in Pigalle", "$$", "Italian", 4.4),
],
"tokyo": [
("Sukiyabashi Jiro", "Legendary 3 Michelin star sushi", "$$$$", "Sushi", 4.9),
("Ichiran Ramen", "Famous tonkotsu ramen chain", "$", "Ramen", 4.5),
("Gonpachi", "The Kill Bill restaurant", "$$$", "Japanese", 4.4),
("Genki Sushi", "Fun conveyor belt sushi", "$$", "Sushi", 4.3),
],
"default": [
("The Local Kitchen", "Farm-to-table dining experience", "$$$", "Local", 4.5),
("Seaside Terrace", "Fresh seafood with views", "$$$", "Seafood", 4.4),
("Downtown Bistro", "Classic comfort food", "$$", "International", 4.3),
("Rooftop Garden", "Panoramic views and cocktails", "$$$", "Modern", 4.6),
]
}
@mcp.tool()
def find_restaurants(city: str, cuisine: str = "local", buffet: bool = False) -> str:
"""Find restaurants or buffets in a city with reservation links."""
# URL encode city
city_clean = city.split(",")[0].strip()
city_lower = city_clean.lower()
city_encoded = urllib.parse.quote(city_clean)
# Get city-specific restaurants or default
restaurants = DESTINATION_RESTAURANTS.get(city_lower, DESTINATION_RESTAURANTS["default"])
results = []
results.append(f"🍽️ **Top Restaurants in {city}**")
results.append("")
results.append("---")
selected = random.sample(restaurants, min(4, len(restaurants)))
price_emojis = {"$": "💵", "$$": "💵💵", "$$$": "💵💵💵", "$$$$": "💵💵💵💵"}
for i, (name, desc, price, cuisine_type, base_rating) in enumerate(selected, 1):
rating = round(base_rating + random.uniform(-0.2, 0.2), 1)
rating = min(5.0, max(4.0, rating))
reviews = random.randint(500, 3000)
# Build booking URLs
restaurant_encoded = urllib.parse.quote(f"{name} {city_clean}")
tripadvisor_url = f"https://www.tripadvisor.com/Search?q={restaurant_encoded}"
opentable_url = f"https://www.opentable.com/s?term={restaurant_encoded}"
results.append("")
results.append(f"### 🍴 Option {i}: {name}")
results.append(f"{desc}")
results.append(f"🍳 {cuisine_type} | {price_emojis.get(price, '')} {price}")
results.append(f"⭐ {rating}/5 ({reviews:,} reviews)")
results.append(f"🔗 [View on TripAdvisor]({tripadvisor_url}) | [Reserve on OpenTable]({opentable_url})")
results.append("")
results.append("---")
results.append("")
results.append(f"💡 **More restaurants:** [Explore {city_clean} dining on TripAdvisor](https://www.tripadvisor.com/Search?q={city_encoded}%20restaurants)")
return "\n".join(results)
if __name__ == "__main__":
mcp.run()
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