nostaigia/ParaTool huggingface dataset:
name: calculate_genotype_frequency
description: Calculate the frequency of homozygous dominant genotype based on the allele frequency using Hardy Weinberg Principle.
parameters: type: dict
properties: allele_frequency: type: float
description: The frequency of the dominant allele in the population.
genotype: type: string
description: The genotype which frequency is needed, default is homozygous dominant. 
enum: AA
Aa
aa
required: allele_frequency
genotype
name: calculate_density
description: Calculate the population density of a specific country in a specific year.
parameters: type: dict
properties: country: type: string
description: The country for which the density needs to be calculated.
year: type: string
description: The year in which the density is to be calculated.
population: type: float
description: The population of the country.
land_area: type: float
description: The land area of the country in square kilometers.
required: country
year
population
land_area
name: ecology_data.precipitation_stats
description: Retrieve precipitation data for a specified location and time period.
parameters: type: dict
properties: location: type: string
description: The name of the location, e.g., 'Amazon rainforest'.
time_frame: type: string
enum: six_months
year
five_years
description: The time period for which data is required.
required: location
time_frame
name: identify_bird
description: Identify a bird species based on certain characteristics.
parameters: type: dict
properties: color: type: string
description: Color of the bird.
habitat: type: string
description: Habitat of the bird.
size: type: string
enum: small
medium
large
description: Size of the bird.
default: small
required: color
habitat
name: forest_growth_forecast
description: Predicts the forest growth over the next N years based on current trends.
parameters: type: dict
properties: location: type: string
description: The location where you want to predict forest growth.
years: type: integer
description: The number of years for the forecast.
include_human_impact: type: boolean
description: Whether or not to include the impact of human activities in the forecast. If not provided, defaults to false.
required: location
years
name: ecology.get_turtle_population
description: Get the population and species of turtles in a specific location.
parameters: type: dict
properties: location: type: string
description: The name of the location.
year: type: integer
description: The year of the data requested. (optional). default is 2000
species: type: boolean
description: Whether to include species information. Default is false. (optional)
required: location
name: calculate_vehicle_emission
description: Calculate the annual carbon emissions produced by a specific type of vehicle based on mileage.
parameters: type: dict
properties: vehicle_type: type: string
description: The type of vehicle. 'gas' refers to a gasoline vehicle, 'diesel' refers to a diesel vehicle, and 'EV' refers to an electric vehicle.
miles_driven: type: integer
description: The number of miles driven per year.
emission_factor: type: float
description: Optional emission factor to calculate emissions. Default factor is set for gas vehicles of 1.4
required: vehicle_type
miles_driven
name: generate_DNA_sequence
description: Generate a random DNA sequence with a specific length and nucleotide preference.
parameters: type: dict
properties: length: type: integer
description: The length of the DNA sequence to be generated.
preferences: type: array
items: type: string
enum: A
T
C
G
description: Preferred nucleotides to include more frequently in the DNA sequence.
required: length
preferences
name: population_projections
description: Calculates the projected population growth based on the current growth rate.
parameters: type: dict
properties: country: type: string
description: The country for which to calculate the population projection.
years: type: integer
description: Number of years for the projection.
growth_rate: type: float
description: Optional parameter to specify the growth rate. Default is current growth rate. of 0.01
required: country
years
name: elephant_population_estimate
description: Estimate future population of elephants given current population and growth rate.
parameters: type: dict
properties: current_population: type: integer
description: The current number of elephants.
growth_rate: type: float
description: The annual population growth rate of elephants.
years: type: integer
description: The number of years to project the population.
required: current_population
growth_rate
years
name: prediction.evolution
description: Predict the evolutionary rate for a specific species for a given timeframe.
parameters: type: dict
properties: species: type: string
description: The species that the evolution rate will be predicted for.
years: type: integer
description: Number of years for the prediction.
model: type: string
description: The model used to make the prediction, options: 'Darwin', 'Lamarck', default is 'Darwin'.
required: species
years
name: restaurant.find_nearby
description: Locate nearby restaurants based on specific dietary preferences.
parameters: type: dict
properties: location: type: string
description: The city and state, e.g. Los Angeles, CA
dietary_preference: type: array
items: type: string
enum: Vegan
Vegetarian
Gluten-free
Dairy-free
Nut-free
description: Dietary preference.
default: Vegan
required: location
name: average_temperature
description: Retrieves the average temperature for a specific location over the defined timeframe.
parameters: type: dict
properties: location: type: string
description: The city to get the average temperature for.
days: type: integer
description: The number of days to get the average temperature for.
temp_unit: type: string
description: The temperature unit ('Celsius' or 'Fahrenheit'). Default is 'Fahrenheit'.
required: location
days
name: create_histogram
description: Create a histogram based on provided data.
parameters: type: dict
properties: data: type: array
items: type: integer
description: The data for which histogram needs to be plotted.
bins: type: integer
description: The number of equal-width bins in the range. Default is 10.
required: data
bins
name: find_restaurants
description: Locate nearby restaurants based on location and food preferences.
parameters: type: dict
properties: location: type: string
description: The specific location or area.
food_type: type: string
description: The type of food preferred.
number: type: integer
description: Number of results to return.
dietary_requirements: type: array
items: type: string
description: Special dietary requirements, e.g. vegan, gluten-free.
default: None
required: location
food_type
number
name: map_routing.fastest_route
description: Finds the fastest route from one location to another, with an option to avoid toll roads.
parameters: type: dict
properties: start_location: type: string
description: The starting location for the journey.
end_location: type: string
description: The destination for the journey.
avoid_tolls: type: boolean
description: Option to avoid toll roads during the journey. default is False
required: start_location
end_location
name: calculate_average
description: Calculates the average of a list of numbers.
parameters: type: dict
properties: numbers: type: array
items: type: integer
description: The list of numbers to calculate the average of.
required: numbers
name: calculate_distance
description: Calculate the distance between two GPS coordinates.
parameters: type: dict
properties: coord1: type: tuple
description: The first coordinate as (latitude, longitude).
items: type: float
coord2: type: tuple
description: The second coordinate as (latitude, longitude).
items: type: float
unit: type: string
description: The unit of distance. Defaults to miles if not specified.
required: coord1
coord2
unit
name: calculate_bmi
description: Calculate the Body Mass Index (BMI) of a person.
parameters: type: dict
properties: weight: type: integer
description: Weight of the person in kilograms.
height: type: integer
description: Height of the person in centimeters.
unit: type: string
description: Optional parameter to choose between 'imperial' and 'metric' systems. Default is 'metric'.
required: weight
height
name: geo_distance.calculate
description: Calculate the geographic distance between two given locations.
parameters: type: dict
properties: start_location: type: string
description: The starting location for the distance calculation.
end_location: type: string
description: The destination location for the distance calculation.
units: type: string
description: Optional. The desired units for the resulting distance ('miles' or 'kilometers'). Defaults to 'miles'.
required: start_location
end_location
name: city_distance.find_shortest
description: Calculates the shortest distance between two cities via available public transportation.
parameters: type: dict
properties: start_city: type: string
description: The city you are starting from.
end_city: type: string
description: The city you are heading to.
transportation: type: string
description: Preferred mode of public transportation. Default is 'bus'.
allow_transfer: type: boolean
description: Allows transfer between different transportation if true. default is False
required: start_city
end_city
name: array_sort
description: Sorts a given list in ascending or descending order.
parameters: type: dict
properties: list: type: array
items: type: integer
description: The list of numbers to be sorted.
order: type: string
enum: ascending
descending
description: Order of sorting. If not specified, it will default to ascending.
required: list
order
name: calculate_BMI
description: Calculate the Body Mass Index (BMI) given a person's weight and height.
parameters: type: dict
properties: weight_kg: type: integer
description: The weight of the person in kilograms.
height_m: type: float
description: The height of the person in meters.
required: weight_kg
height_m
name: employee.fetch_data
description: Fetches the detailed data for a specific employee in a given company.
parameters: type: dict
properties: company_name: type: string
description: The name of the company.
employee_id: type: integer
description: The unique ID of the employee.
data_field: type: array
items: type: string
enum: Personal Info
Job History
Payroll
Attendance
description: Fields of data to be fetched for the employee (Optional).
default: Personal Info
required: company_name
employee_id
name: imdb.find_movies_by_actor
description: Searches the database to find all movies by a specific actor within a certain year.
parameters: type: dict
properties: actor_name: type: string
description: The name of the actor.
year: type: integer
description: The specific year to search in.
category: type: string
description: The category of the film (e.g. Drama, Comedy, etc). This parameter is optional.
default: Drama
required: actor_name
year
name: get_theater_movie_releases
description: Retrieve the list of movie releases in specific theaters for a specified period.
parameters: type: dict
properties: location: type: string
description: The location of the theaters.
timeframe: type: integer
description: The number of days for which releases are required from current date.
format: type: string
description: Format of movies - could be 'IMAX', '2D', '3D', '4DX' etc. This is an optional parameter.
default: IMAX
required: location
timeframe
name: update_user_info
description: Update user information in the database.
parameters: type: dict
properties: user_id: type: integer
description: The user ID of the customer.
update_info: type: dict
properties: name: type: string
description: The customer's updated name.
email: type: string
description: The customer's updated email.
description: The new information to update.
database: type: string
description: The database where the user's information is stored.
default: CustomerInfo
required: user_id
update_info
name: calc_area_triangle
description: Calculate the area of a triangle with the formula area = 0.5 * base * height.
parameters: type: dict
properties: base: type: integer
description: The length of the base of the triangle in meters.
height: type: integer
description: The perpendicular height of the triangle from the base to the opposite vertex in meters.
required: base
height
name: math.factorial
description: Calculate the factorial of a given number.
parameters: type: dict
properties: number: type: integer
description: The number to compute factorial.
required: number
name: calculate_clock_angle
description: Calculate the angle between the hour and minute hands of a clock at a given time.
parameters: type: dict
properties: hours: type: integer
description: The hour on the clock face.
minutes: type: integer
description: The minutes on the clock face.
round_to: type: integer
description: The number of decimal places to round the result to, default is 2.
required: hours
minutes
name: plot_sine_wave
description: Plot a sine wave for a given frequency in a given range.
parameters: type: dict
properties: start_range: type: integer
description: Start of the range in radians.
end_range: type: integer
description: End of the range in radians.
frequency: type: integer
description: Frequency of the sine wave in Hz.
amplitude: type: integer
description: Amplitude of the sine wave. Default is 1.
phase_shift: type: integer
description: Phase shift of the sine wave in radians. Default is 0.
required: start_range
end_range
frequency
name: light_travel_time
description: Calculate the time taken for light to travel from a celestial body to another.
parameters: type: dict
properties: distance_in_light_years: type: float
description: The distance between the two celestial bodies in light years.
speed_of_light: type: integer
description: The speed of light in vacuum, in m/s. Default value is 299792458 m/s.
required: distance_in_light_years
name: calculate_speed
description: Calculate the speed of an object based on the distance travelled and the time taken.
parameters: type: dict
properties: distance: type: integer
description: The distance the object travelled in meters.
time: type: integer
description: The time it took for the object to travel in seconds.
to_unit: type: string
description: The unit in which the speed should be calculated, default is m/s.
required: distance
time
name: calculate_distance
description: Calculate the distance between two celestial bodies.
parameters: type: dict
properties: body1: type: string
description: The first celestial body.
body2: type: string
description: The second celestial body.
unit: type: string
description: The unit of measurement, default is 'kilometers'.
required: body1
body2
name: mathematics.calculate_area_under_curve
description: Calculate the area under the curve for a given polynomial function within a specified interval.
parameters: type: dict
properties: polynomial: type: array
items: type: integer
description: The coefficients of the polynomial, in decreasing order of exponent, where the first element is the coefficient for x^n, the second element is the coefficient for x^(n-1), and so on. The last element is the constant term.
limits: type: array
items: type: integer
description: A list of two numbers specifying the lower and upper limit for the integration interval.
required: polynomial
limits
name: geometry.area_triangle
description: Calculate the area of a triangle.
parameters: type: dict
properties: base: type: integer
description: The length of the base of the triangle.
height: type: integer
description: The height of the triangle from the base.
unit: type: string
description: The measurement unit for the area. Defaults to square meters.
required: base
height
name: math.power
description: Calculate the power of one number raised to another.
parameters: type: dict
properties: base: type: integer
description: The base number.
exponent: type: integer
description: The exponent.
mod: type: float
description: The modulus. Default is None. Calculates pow(base, exponent) % mod when provided.
required: base
exponent
name: train_random_forest_classifier
description: Train a Random Forest classifier with the specified parameters.
parameters: type: dict
properties: dataset: type: string
description: The dataset to train the classifier on.
max_depth: type: integer
description: The maximum depth of the trees in the forest.
n_estimators: type: integer
description: The number of trees in the forest.
required: dataset
max_depth
n_estimators
name: calculate_bmi
description: Calculate the Body Mass Index (BMI) for a person based on their weight and height.
parameters: type: dict
properties: weight: type: integer
description: The weight of the person in kilograms.
height: type: integer
description: The height of the person in centimeters.
system: type: string
description: The system of units to be used, 'metric' or 'imperial'. Default is 'metric'.
required: weight
height
name: run_linear_regression
description: Build a linear regression model using given predictor variables and a target variable.
parameters: type: dict
properties: predictors: type: array
items: type: string
description: Array containing the names of predictor variables.
target: type: string
description: The name of target variable.
standardize: type: boolean
description: Option to apply standardization on the predictors. Defaults to False.
required: predictors
target
name: random_forest.train
description: Train a Random Forest Model on given data
parameters: type: dict
properties: n_estimators: type: integer
description: The number of trees in the forest.
max_depth: type: integer
description: The maximum depth of the tree.
data: type: string
description: The training data for the model.
required: n_estimators
max_depth
data
name: predict_house_price
description: Predict the price of a house in a given area based on number of bedrooms, bathrooms and area.
parameters: type: dict
properties: bedrooms: type: integer
description: The number of bedrooms in the house.
bathrooms: type: integer
description: The number of bathrooms in the house.
area: type: integer
description: The area of the house in square feet.
location: type: string
description: The location of the house.
required: bedrooms
bathrooms
area
location
name: random.normalvariate
description: Generates a random number from a normal distribution given the mean and standard deviation.
parameters: type: dict
properties: mu: type: integer
description: Mean of the normal distribution.
sigma: type: integer
description: Standard deviation of the normal distribution.
required: mu
sigma
name: probability.dice_roll
description: Calculate the probability of rolling a certain number on a six-sided die a certain number of times in a row.
parameters: type: dict
properties: desired_number: type: integer
description: The number you want to roll.
number_of_rolls: type: integer
description: How many times you want to roll that number in a row.
die_sides: type: integer
description: The number of sides on the die (optional; default is 6).
required: desired_number
number_of_rolls
name: prob_dist.binomial
description: Compute the probability of having 'success' outcome from binomial distribution.
parameters: type: dict
properties: trials: type: integer
description: The number of independent experiments.
successes: type: integer
description: The number of success events.
p: type: float
description: The probability of success on any given trial, defaults to 0.5
required: trials
successes
name: calculate_binomial_probability
description: Calculates the binomial probability given the number of trials, successes and the probability of success on an individual trial.
parameters: type: dict
properties: number_of_trials: type: integer
description: The total number of trials.
number_of_successes: type: integer
description: The desired number of successful outcomes.
probability_of_success: type: float
description: The probability of a successful outcome on any given trial.
default: 0.5
required: number_of_trials
number_of_successes
name: probability_of_event
description: Calculates the probability of an event.
parameters: type: dict
properties: success_outcomes: type: integer
description: The number of successful outcomes.
total_outcomes: type: integer
description: The total number of possible outcomes.
format_as_ratio: type: boolean
description: When true, formats the output as a ratio instead of a decimal. Default is false.
required: success_outcomes
total_outcomes
name: calc_binomial_prob
description: Calculates the probability of an outcome based on the binomial distribution
parameters: type: dict
properties: num_trials: type: integer
description: Number of independent experiments.
num_success: type: integer
description: Number of times the event of interest has occurred.
prob_success: type: float
description: Probability of the event of interest on any single experiment.
required: num_trials
num_success
prob_success
name: chi_squared_test
description: Performs a Chi-Squared test for independence on a 2x2 contingency table.
parameters: type: dict
properties: table: type: array
items: type: integer
description: A 2x2 contingency table presented in array form.
alpha: type: float
description: Significance level for the Chi-Squared test. Default is 0.05.
required: table
name: t_test
description: Perform a statistical t-test to check if the means of two independent datasets are statistically different.
parameters: type: dict
properties: dataset_A: type: array
items: type: integer
description: Dataset A for comparison.
dataset_B: type: array
items: type: integer
description: Dataset B for comparison.
alpha: type: float
description: Significance level for the test. Default is 0.05.
required: dataset_A
dataset_B
name: predict_house_price
description: Predict house price based on area, number of rooms and year of construction.
parameters: type: dict
properties: area: type: integer
description: Area of the house in square feet.
rooms: type: integer
description: Number of rooms in the house.
year: type: integer
description: Year when the house was constructed.
location: type: string
description: The location or city of the house.
required: area
rooms
year
location
name: linear_regression.get_r_squared
description: Calculate the coefficient of determination of a regression model.
parameters: type: dict
properties: dataset_path: type: string
description: Path to the CSV dataset file.
independent_variables: type: array
items: type: string
description: The independent variables to use in the regression model.
dependent_variable: type: string
description: The dependent variable to predict in the regression model.
required: dataset_path
independent_variables
dependent_variable
name: finance.calculate_quarterly_dividend_per_share
description: Calculate quarterly dividend per share for a company given total dividend payout and outstanding shares
parameters: type: dict
properties: total_payout: type: integer
description: The total amount of dividends paid out in USD
outstanding_shares: type: integer
description: Total number of outstanding shares
required: total_payout
outstanding_shares
name: calculate_discounted_cash_flow
description: Calculate the discounted cash flow of a bond for a given annual coupon payment, time frame and discount rate.
parameters: type: dict
properties: coupon_payment: type: integer
description: The annual coupon payment.
period: type: integer
description: The time frame in years for which coupon payment is made.
discount_rate: type: float
description: The discount rate.
face_value: type: integer
description: The face value of the bond, default is $1000.
required: coupon_payment
period
discount_rate
name: calculate_compound_interest
description: Calculate compound interest for an initial principal amount.
parameters: type: dict
properties: principal: type: integer
description: The principal amount that the interest is applied to.
rate: type: float
description: The annual interest rate. Enter as a decimal. E.g, 5% is 0.05
time: type: integer
description: The time the money is invested for in years.
n: type: integer
description: The number of times that interest is compounded per time period. Default is 1.
required: principal
rate
time
name: calculate_return_on_equity
description: Calculate a company's return on equity based on its net income, shareholder's equity, and dividends paid.
parameters: type: dict
properties: net_income: type: integer
description: The company's net income.
shareholder_equity: type: integer
description: The company's total shareholder's equity.
dividends_paid: type: integer
description: The total dividends paid by the company. Optional. If not given, assumes it's 0 as default.
required: net_income
shareholder_equity
name: finance.predict_future_value
description: Calculate the future value of an investment given its present value, interest rate, the number of compounding periods per year, and the time horizon.
parameters: type: dict
properties: present_value: type: integer
description: The present value of the investment.
annual_interest_rate: type: float
description: The annual interest rate of the investment.
compounding_periods_per_year: type: integer
description: The number of times that interest is compounded per year. Default is 1 (annually).
time_years: type: integer
description: The investment horizon in years.
required: present_value
annual_interest_rate
time_years
name: investment.predictProfit
description: Predict the profit for given investment after specified number of years.
parameters: type: dict
properties: investment_amount: type: integer
description: The amount invested in dollars.
annual_return: type: float
description: The annual return rate of the investment.
years: type: integer
description: The time period in years for which the investment is made.
required: investment_amount
annual_return
years
name: calculate_return_on_investment
description: Calculate the return on investment for a given stock based on its purchase price, sale price, and any dividends received.
parameters: type: dict
properties: purchase_price: type: integer
description: The price the stock was bought at.
sale_price: type: integer
description: The price the stock was sold at.
dividend: type: integer
description: Any dividends received from the stock.
default: 0
required: purchase_price
sale_price
name: portfolio_future_value
description: Calculate the future value of an investment in a specific stock based on the invested amount, expected annual return and number of years.
parameters: type: dict
properties: stock: type: string
description: The ticker symbol of the stock.
invested_amount: type: integer
description: The invested amount in USD.
expected_annual_return: type: float
description: The expected annual return on investment as a decimal. E.g. 5% = 0.05
years: type: integer
description: The number of years for which the investment is made.
required: stock
invested_amount
expected_annual_return
years
name: calculate_cagr
description: Calculate the Compound Annual Growth Rate (CAGR) given an initial investment value, a final investment value, and the number of years.
parameters: type: dict
properties: initial_value: type: integer
description: The initial investment value.
final_value: type: integer
description: The final investment value.
period_in_years: type: integer
description: The period of the investment in years.
required: initial_value
final_value
period_in_years
name: get_metal_price
description: Retrieve the current price for a specified metal and measure.
parameters: type: dict
properties: metal: type: string
description: The metal whose price needs to be fetched.
measure: type: string
description: The measure unit for price, like 'ounce' or 'kg'.
required: metal
measure
name: get_stock_price
description: Get the closing stock price for a specific company on a specified date.
parameters: type: dict
properties: company_name: type: string
description: Name of the company.
date: type: string
description: Date of when to get the stock price. Format: yyyy-mm-dd.
exchange: type: string
description: Name of the stock exchange market where the company's stock is listed. Default is 'NASDAQ'
required: company_name
date
name: get_stock_price
description: Retrieve the stock price for a specific company and time frame.
parameters: type: dict
properties: company: type: string
description: The ticker symbol of the company.
days: type: integer
description: Number of past days for which the stock price is required.
exchange: type: string
description: The stock exchange where the company is listed, default is NYSE
required: company
days
name: stock_price
description: Get stock price data for a given company over a specified number of days.
parameters: type: dict
properties: company: type: string
description: The company name.
days: type: integer
description: The number of previous days to retrieve data for.
data_type: type: string
description: The type of price data to retrieve (e.g., 'Open', 'Close', 'High', 'Low'). Default is 'Close'.
required: company
days
name: get_stock_prices
description: Retrieves stock prices for specified companies and duration.
parameters: type: dict
properties: companies: type: array
items: type: string
description: List of companies to retrieve stock prices for.
duration: type: string
description: Time duration to retrieve stock prices for. E.g., '1 week', '2 weeks', '1 month', etc.
required: companies
duration
name: finance.calculate_future_value
description: Calculate the future value of an investment given an initial investment, annual rate of return, and a time frame.
parameters: type: dict
properties: initial_investment: type: integer
description: The initial investment amount.
rate_of_return: type: float
description: The annual rate of return.
years: type: integer
description: The time frame of the investment in years.
contribution: type: integer
description: Optional: Additional regular contributions. Default is 0.
required: initial_investment
rate_of_return
years
name: estimate_population
description: Estimate the population of a particular species in a given country.
parameters: type: dict
properties: species: type: string
description: The species for which population needs to be estimated.
country: type: string
description: The country where the species lives.
year: type: integer
description: The year for which population estimate is sought. Default is 2020.
required: species
country
name: calculate_emission_savings
description: Calculate potential greenhouse gas emissions saved by switching to renewable energy sources.
parameters: type: dict
properties: energy_type: type: string
description: Type of the renewable energy source.
usage_duration: type: integer
description: Usage duration in months.
region: type: string
description: The region where you use energy.
default: West
required: energy_type
usage_duration
name: get_air_quality
description: Retrieve real-time air quality and pollution data for a specific location.
parameters: type: dict
properties: location: type: string
description: The city that you want to get the air quality data for.
detail: type: boolean
description: If true, additional data like PM2.5, PM10, ozone levels, and pollution sources will be retrieved. the value is set to false to default.
historical: type: string
description: Optional date (in 'YYYY-MM-DD' format) to retrieve historical data.
default: today
required: location
name: get_traffic_info
description: Retrieve current traffic conditions for a specified route.
parameters: type: dict
properties: start_location: type: string
description: The starting point of the route.
end_location: type: string
description: The destination of the route.
mode: type: string
enum: driving
walking
bicycling
transit
description: Preferred method of transportation, default to 'driving'.
required: start_location
end_location
name: parks.find_nearby
description: Locate nearby parks based on specific criteria like tennis court availability.
parameters: type: dict
properties: location: type: string
description: The city and state, e.g. London, UK
amenities: type: array
items: type: string
enum: Tennis Court
Picnic Area
Playground
Running Track
description: Preferred amenities in park.
default: Playground
required: location
name: calculate_shortest_distance
description: Calculate the shortest driving distance between two locations.
parameters: type: dict
properties: start_location: type: string
description: The starting location for the drive.
end_location: type: string
description: The destination location for the drive.
route_preference: type: string
enum: Shortest
Scenic
description: The preferred type of route.
required: start_location
end_location
route_preference
name: public_library.find_nearby
description: Locate nearby public libraries based on specific criteria like English fiction availability and Wi-Fi.
parameters: type: dict
properties: location: type: string
description: The city and state, e.g. Boston, MA
facilities: type: array
items: type: string
enum: Wi-Fi
Reading Room
Fiction
Children Section
Cafe
description: Facilities and sections in public library.
required: location
facilities
name: get_news
description: Fetches the latest news on a specific topic.
parameters: type: dict
properties: topic: type: string
description: The subject for the news topic.
quantity: type: integer
description: Number of articles to fetch.
region: type: string
description: The geographical region for the news (Optional). default is 'USA'
required: topic
quantity
name: send_email
description: Send an email to the specified email address.
parameters: type: dict
properties: to: type: string
description: The email address to send to.
subject: type: string
description: The subject of the email.
body: type: string
description: The body content of the email.
cc: type: string
description: The email address to carbon copy. default is ''.
bcc: type: string
description: The email address to blind carbon copy. the value is set to '' for default.
required: to
subject
body
name: event_finder.find_upcoming
description: Find upcoming events of a specific genre in a given location.
parameters: type: dict
properties: location: type: string
description: The city and state where the search will take place, e.g. New York, NY.
genre: type: string
description: The genre of events.
days_ahead: type: integer
description: The number of days from now to include in the search.
default: 7
required: location
genre
name: movie_details.brief
description: This function retrieves a brief about a specified movie.
parameters: type: dict
properties: title: type: string
description: Title of the movie
extra_info: type: boolean
description: Option to get additional information like Director, Cast, Awards etc.
default: false
required: title
name: get_lawsuit_details
description: Retrieve details of a lawsuit based on its case number and court location.
parameters: type: dict
properties: case_number: type: string
description: Case number of the lawsuit.
court_location: type: string
description: The location of the court where the lawsuit was filed.
with_verdict: type: boolean
description: Flag to include verdict details if available. Default is False
required: case_number
court_location
name: lawsuit_info
description: Retrieves details of a lawsuit given a case number
parameters: type: dict
properties: case_number: type: string
description: The unique identifier of the lawsuit case
year: type: integer
description: The year in which the lawsuit case was initiated
optional: yes
default: 2000
location: type: string
description: The location or court jurisdiction where the case was filed.
optional: yes
default: New York
required: case_number
name: lawsuit_search
description: Retrieve all lawsuits involving a particular entity from specified jurisdiction.
parameters: type: dict
properties: entity: type: string
description: The entity involved in lawsuits.
county: type: string
description: The jurisdiction for the lawsuit search.
state: type: string
description: The state for the lawsuit search. Default is California.
required: entity
county
name: get_current_weather
description: Retrieves the current temperature and humidity for a specific location.
parameters: type: dict
properties: location: type: string
description: The location to get the weather for.
include_temperature: type: boolean
description: Whether to include the temperature in the result. Default is true.
include_humidity: type: boolean
description: Whether to include the humidity in the result. Default is true.
required: location