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Model Context Protocol (MCP) support with new use case (#42)
* initial mcp * food ordering with mcp * prompt eng * splitting out goals and updating docs * a diff so I can get tests from codex * a diff so I can get tests from codex * oops, missing files * tests, file formatting * readme and setup updates * setup.md link fixes * readme change * readme change * readme change * stripe food setup script * single agent mode default * prompt engineering for better multi agent performance * performance should be greatly improved * Update goals/finance.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update activities/tool_activities.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * co-pilot PR suggested this change, and now fixed it * stronger wording around json format response * formatting * moved docs to dir * moved image assets under docs * cleanup env example, stripe guidance * cleanup --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Steve Androulakis
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GitHub
GitHub
parent
1811e4cf59
commit
5d55a9fe80
@@ -1,6 +1,7 @@
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import http.client
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import json
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import os
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import random
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import urllib.parse
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from dotenv import load_dotenv
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@@ -174,45 +175,166 @@ def search_flights_real_api(
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}
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def generate_smart_flights(origin: str, destination: str) -> list:
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"""
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Generate realistic flight options with smart pricing based on origin and destination.
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"""
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# Common airlines for different regions
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airlines_by_region = {
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"domestic_us": [
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{"name": "American Airlines", "code": "AA"},
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{"name": "United Airlines", "code": "UA"},
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{"name": "Delta Airlines", "code": "DL"},
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{"name": "Southwest Airlines", "code": "WN"},
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],
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"us_international": [
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{"name": "American Airlines", "code": "AA"},
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{"name": "United Airlines", "code": "UA"},
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{"name": "Delta Airlines", "code": "DL"},
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{"name": "Virgin Atlantic", "code": "VS"},
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],
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"australia_nz": [
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{"name": "Qantas", "code": "QF"},
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{"name": "Jetstar", "code": "JQ"},
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{"name": "Virgin Australia", "code": "VA"},
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{"name": "Air New Zealand", "code": "NZ"},
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],
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"international": [
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{"name": "American Airlines", "code": "AA"},
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{"name": "United Airlines", "code": "UA"},
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{"name": "Delta Airlines", "code": "DL"},
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{"name": "Air New Zealand", "code": "NZ"},
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{"name": "Qantas", "code": "QF"},
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{"name": "Singapore Airlines", "code": "SQ"},
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],
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}
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# Determine route type and base pricing
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origin_lower = origin.lower()
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dest_lower = destination.lower()
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# Australia/NZ cities
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anz_cities = [
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"sydney",
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"melbourne",
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"syd",
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"mel",
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"auckland",
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"akl",
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"wellington",
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"wlg",
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"brisbane",
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"bne",
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"perth",
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"per",
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]
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# US cities
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us_cities = [
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"los angeles",
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"lax",
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"san francisco",
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"sfo",
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"new york",
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"nyc",
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"jfk",
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"chicago",
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"ord",
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"miami",
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"mia",
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]
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is_origin_anz = any(city in origin_lower for city in anz_cities)
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is_dest_anz = any(city in dest_lower for city in anz_cities)
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is_origin_us = any(city in origin_lower for city in us_cities)
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is_dest_us = any(city in dest_lower for city in us_cities)
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# Determine airline pool and base price
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if (is_origin_us and is_dest_anz) or (is_origin_anz and is_dest_us):
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# Trans-Pacific routes
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airline_pool = airlines_by_region["international"]
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base_price = random.randint(1200, 1800)
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elif is_origin_anz and is_dest_anz:
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# Australia/NZ domestic
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airline_pool = airlines_by_region["australia_nz"]
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base_price = random.randint(300, 600)
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elif is_origin_us and is_dest_us:
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# US domestic
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airline_pool = airlines_by_region["domestic_us"]
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base_price = random.randint(200, 800)
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else:
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# General international
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airline_pool = airlines_by_region["international"]
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base_price = random.randint(800, 1500)
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# Generate 3-4 flight options
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num_flights = random.randint(3, 4)
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results = []
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used_airlines = set()
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for i in range(num_flights):
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# Pick unique airline
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available_airlines = [a for a in airline_pool if a["name"] not in used_airlines]
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if not available_airlines:
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available_airlines = airline_pool # Reset if we run out
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airline = random.choice(available_airlines)
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used_airlines.add(airline["name"])
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# Generate flight numbers
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outbound_num = random.randint(100, 999)
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return_num = random.randint(100, 999)
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# Price variation (cheaper airlines get lower prices)
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price_multiplier = 1.0
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if "Southwest" in airline["name"] or "Jetstar" in airline["name"]:
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price_multiplier = 0.7
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elif "Virgin" in airline["name"]:
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price_multiplier = 0.85
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elif "Singapore" in airline["name"]:
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price_multiplier = 1.2
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# Add some random variation
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price_variation = random.uniform(0.9, 1.1)
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final_price = round(base_price * price_multiplier * price_variation, 2)
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results.append(
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{
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"operating_carrier": airline["name"],
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"outbound_flight_code": f"{airline['code']}{outbound_num}",
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"price": final_price,
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"return_flight_code": f"{airline['code']}{return_num}",
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"return_operating_carrier": airline["name"],
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}
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)
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# Sort by price
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results.sort(key=lambda x: x["price"])
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return results
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def search_flights(args: dict) -> dict:
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"""
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Returns example flight search results in the requested JSON format.
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Search for flights. Uses real API if RAPIDAPI_KEY is available, otherwise generates smart mock data.
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"""
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load_dotenv(override=True)
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api_key = os.getenv("RAPIDAPI_KEY")
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origin = args.get("origin")
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destination = args.get("destination")
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if not origin or not destination:
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return {"error": "Both origin and destination are required"}
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# If API key is available, use the real API
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if api_key and api_key != "YOUR_DEFAULT_KEY":
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return search_flights_real_api(args)
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# Otherwise, generate smart mock data
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results = generate_smart_flights(origin, destination)
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return {
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"currency": "USD",
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"destination": f"{destination}",
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"origin": f"{origin}",
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"results": [
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{
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"operating_carrier": "American Airlines",
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"outbound_flight_code": "AA203",
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"price": 1262.51,
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"return_flight_code": "AA202",
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"return_operating_carrier": "American Airlines",
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},
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{
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"operating_carrier": "Air New Zealand",
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"outbound_flight_code": "NZ488",
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"price": 1396.00,
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"return_flight_code": "NZ527",
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"return_operating_carrier": "Air New Zealand",
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},
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{
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"operating_carrier": "United Airlines",
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"outbound_flight_code": "UA100",
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"price": 1500.00,
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"return_flight_code": "UA101",
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"return_operating_carrier": "United Airlines",
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},
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{
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"operating_carrier": "Delta Airlines",
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"outbound_flight_code": "DL200",
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"price": 1600.00,
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"return_flight_code": "DL201",
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"return_operating_carrier": "Delta Airlines",
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},
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],
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"destination": destination,
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"origin": origin,
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"results": results,
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}
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