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from mcp.server.fastmcp import FastMCP |
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import random |
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import urllib.parse |
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mcp = FastMCP("DiningAgent") |
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DESTINATION_RESTAURANTS = { |
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"dubai": [ |
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("At.mosphere", "World's highest restaurant at Burj Khalifa", "$$$$", "Fine Dining", 4.7), |
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("Pierchic", "Overwater seafood restaurant at Al Qasr", "$$$$", "Seafood", 4.6), |
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("Ossiano", "Underwater dining at Atlantis", "$$$$", "Seafood", 4.8), |
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("Al Hadheerah", "Arabian desert dining with live entertainment", "$$$", "Arabic", 4.5), |
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("Nusr-Et Steakhouse", "Salt Bae's famous steakhouse", "$$$$", "Steakhouse", 4.4), |
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("Arabian Tea House", "Traditional Emirati cuisine", "$$", "Local", 4.6), |
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("Ravi Restaurant", "Famous Pakistani street food", "$", "Pakistani", 4.5), |
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], |
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"paris": [ |
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("Le Jules Verne", "Michelin-starred Eiffel Tower dining", "$$$$", "French", 4.5), |
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("Café de Flore", "Historic Left Bank café", "$$", "French Café", 4.3), |
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("L'Ambroisie", "3 Michelin star classic French", "$$$$", "Fine Dining", 4.9), |
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("Pink Mamma", "Trendy Italian in Pigalle", "$$", "Italian", 4.4), |
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], |
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"tokyo": [ |
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("Sukiyabashi Jiro", "Legendary 3 Michelin star sushi", "$$$$", "Sushi", 4.9), |
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("Ichiran Ramen", "Famous tonkotsu ramen chain", "$", "Ramen", 4.5), |
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("Gonpachi", "The Kill Bill restaurant", "$$$", "Japanese", 4.4), |
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("Genki Sushi", "Fun conveyor belt sushi", "$$", "Sushi", 4.3), |
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], |
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"default": [ |
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("The Local Kitchen", "Farm-to-table dining experience", "$$$", "Local", 4.5), |
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("Seaside Terrace", "Fresh seafood with views", "$$$", "Seafood", 4.4), |
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("Downtown Bistro", "Classic comfort food", "$$", "International", 4.3), |
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("Rooftop Garden", "Panoramic views and cocktails", "$$$", "Modern", 4.6), |
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] |
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} |
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@mcp.tool() |
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def find_restaurants(city: str, cuisine: str = "local", buffet: bool = False) -> str: |
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"""Find restaurants or buffets in a city with reservation links.""" |
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city_clean = city.split(",")[0].strip() |
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city_lower = city_clean.lower() |
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city_encoded = urllib.parse.quote(city_clean) |
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restaurants = DESTINATION_RESTAURANTS.get(city_lower, DESTINATION_RESTAURANTS["default"]) |
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results = [] |
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results.append(f"🍽️ **Top Restaurants in {city}**") |
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results.append("") |
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results.append("---") |
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selected = random.sample(restaurants, min(4, len(restaurants))) |
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price_emojis = {"$": "💵", "$$": "💵💵", "$$$": "💵💵💵", "$$$$": "💵💵💵💵"} |
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for i, (name, desc, price, cuisine_type, base_rating) in enumerate(selected, 1): |
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rating = round(base_rating + random.uniform(-0.2, 0.2), 1) |
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rating = min(5.0, max(4.0, rating)) |
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reviews = random.randint(500, 3000) |
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restaurant_encoded = urllib.parse.quote(f"{name} {city_clean}") |
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tripadvisor_url = f"https://www.tripadvisor.com/Search?q={restaurant_encoded}" |
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opentable_url = f"https://www.opentable.com/s?term={restaurant_encoded}" |
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results.append("") |
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results.append(f"### 🍴 Option {i}: {name}") |
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results.append(f"{desc}") |
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results.append(f"🍳 {cuisine_type} | {price_emojis.get(price, '')} {price}") |
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results.append(f"⭐ {rating}/5 ({reviews:,} reviews)") |
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results.append(f"🔗 [View on TripAdvisor]({tripadvisor_url}) | [Reserve on OpenTable]({opentable_url})") |
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results.append("") |
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results.append("---") |
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results.append("") |
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results.append(f"💡 **More restaurants:** [Explore {city_clean} dining on TripAdvisor](https://www.tripadvisor.com/Search?q={city_encoded}%20restaurants)") |
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return "\n".join(results) |
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if __name__ == "__main__": |
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mcp.run() |
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