Python Functions — Writing Your Own Tools

A function is a reusable chunk of code with a name. You'll write three, add type hints, and let AI suggest better names and docstrings.

30 min · Reviewed 2026

def name(): the magic word

A function packages logic so you can call it by name anywhere. Name it clearly and your code reads like a story. Name it 'do_stuff' and your future self will curse your past self.

def greet(name: str, enthusiasm: int = 1) -> str:
    """Return a friendly greeting.

    Args:
        name: the person to greet
        enthusiasm: number of exclamation marks (default 1)
    """
    bangs = "!" * enthusiasm
    return f"Hello, {name}{bangs}"

print(greet("Maya"))
print(greet("Maya", enthusiasm=5))Type hints (: str, -> str) are optional but massively improve both human and AI understanding.

Parameters, arguments, defaults

A parameter is the name in the definition (name, enthusiasm)
An argument is the value you pass when calling ("Maya", 5)
Defaults let callers skip arguments — use them to keep simple calls simple
Keyword arguments (enthusiasm=5) make calls self-documenting

Return vs. print — the classic beginner mix-up

def add_broken(a, b):
    print(a + b)           # shows the answer — but returns None

def add_good(a, b):
    return a + b           # gives the answer back to the caller

result = add_broken(2, 3)  # prints 5, but result is None
total = add_good(2, 3)     # total is 5 — usable

print(result * 2)          # TypeError: NoneType
print(total * 2)           # 10If a function needs to be usable in another function, it must return, not print.

Mini-exercise: price calculator

Write calc_total(items: list[float], tax_rate: float = 0.08) -> float
Return the sum of items times (1 + tax_rate), rounded to 2 decimals
Add a docstring
Call it with [3.50, 4.25, 1.99] and print the result
Ask your AI assistant to review your code and suggest improvements

Loose function	Tight function
def do(x)	def fetch_user(user_id: int) -> dict
No docstring	Docstring with Args and Returns
Prints results	Returns results
Hard to test	Easy to test

Big idea: a function is a small contract. Its name says what it does, its signature says how to use it, its docstring says why. AI pair-programming gets dramatically better when you tighten those three things up first.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-prog-python-functions-builders

What is the core idea behind "Python Functions — Writing Your Own Tools"?
1. A function is a reusable chunk of code with a name. You'll write three, add type hints, and let AI suggest better names and docstrings.
2. An agent is a loop: model decides, tool runs, model reads result, decides again.
3. f-string
4. Async lets your program make 100 API calls at once instead of one at a time.
Which term best describes a foundational idea in "Python Functions — Writing Your Own Tools"?
1. parameter
2. function
3. return
4. type hint
A learner studying Python Functions — Writing Your Own Tools would need to understand which concept?
1. function
2. return
3. parameter
4. type hint
Which of these is directly relevant to Python Functions — Writing Your Own Tools?
1. function
2. parameter
3. type hint
4. return
Which of the following is a key point about Python Functions — Writing Your Own Tools?
1. A parameter is the name in the definition (name, enthusiasm)
2. An argument is the value you pass when calling ("Maya", 5)
3. Defaults let callers skip arguments — use them to keep simple calls simple
4. Keyword arguments (enthusiasm=5) make calls self-documenting
Which of these does NOT belong in a discussion of Python Functions — Writing Your Own Tools?
1. Defaults let callers skip arguments — use them to keep simple calls simple
2. An argument is the value you pass when calling ("Maya", 5)
3. An agent is a loop: model decides, tool runs, model reads result, decides again.
4. A parameter is the name in the definition (name, enthusiasm)
Which statement is accurate regarding Python Functions — Writing Your Own Tools?
1. Return the sum of items times (1 + tax_rate), rounded to 2 decimals
2. Add a docstring
3. Write calc_total(items: list[float], tax_rate: float = 0.08) -> float
4. Call it with [3.50, 4.25, 1.99] and print the result
Which of these does NOT belong in a discussion of Python Functions — Writing Your Own Tools?
1. Add a docstring
2. Return the sum of items times (1 + tax_rate), rounded to 2 decimals
3. An agent is a loop: model decides, tool runs, model reads result, decides again.
4. Write calc_total(items: list[float], tax_rate: float = 0.08) -> float
What is the key insight about "Docstrings make AI smarter" in the context of Python Functions — Writing Your Own Tools?
1. A docstring (the triple-quoted string at the top of a function) tells both humans and AI what the function does.
2. An agent is a loop: model decides, tool runs, model reads result, decides again.
3. f-string
4. Async lets your program make 100 API calls at once instead of one at a time.
What is the recommended tip about "Review before you run" in the context of Python Functions — Writing Your Own Tools?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. AI code is a first draft. Read through it, understand each line, and test on sample inputs before merging into real proj…
3. f-string
4. Async lets your program make 100 API calls at once instead of one at a time.
Which statement accurately describes an aspect of Python Functions — Writing Your Own Tools?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. f-string
3. A function packages logic so you can call it by name anywhere. Name it clearly and your code reads like a story.
4. Async lets your program make 100 API calls at once instead of one at a time.
What does working with Python Functions — Writing Your Own Tools typically involve?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. f-string
3. Async lets your program make 100 API calls at once instead of one at a time.
4. Big idea: a function is a small contract. Its name says what it does, its signature says how to use it, its docstring says why.
Which best describes the scope of "Python Functions — Writing Your Own Tools"?
1. It focuses on A function is a reusable chunk of code with a name. You'll write three, add type hints, and let AI s
2. It is unrelated to ai-coding workflows
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about Python Functions — Writing Your Own Tools?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. Parameters, arguments, defaults
3. f-string
4. Async lets your program make 100 API calls at once instead of one at a time.
Which section heading best belongs in a lesson about Python Functions — Writing Your Own Tools?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. f-string
3. Return vs. print — the classic beginner mix-up
4. Async lets your program make 100 API calls at once instead of one at a time.

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Tendril · Builders · AI-Assisted Coding

Python Functions — Writing Your Own Tools

A function is a reusable chunk of code with a name. You'll write three, add type hints, and let AI suggest better names and docstrings.

30 min · Reviewed 2026

def name(): the magic word

A function packages logic so you can call it by name anywhere. Name it clearly and your code reads like a story. Name it 'do_stuff' and your future self will curse your past self.

def greet(name: str, enthusiasm: int = 1) -> str:
    """Return a friendly greeting.

    Args:
        name: the person to greet
        enthusiasm: number of exclamation marks (default 1)
    """
    bangs = "!" * enthusiasm
    return f"Hello, {name}{bangs}"

print(greet("Maya"))
print(greet("Maya", enthusiasm=5))Type hints (: str, -> str) are optional but massively improve both human and AI understanding.

Parameters, arguments, defaults

A parameter is the name in the definition (name, enthusiasm)
An argument is the value you pass when calling ("Maya", 5)
Defaults let callers skip arguments — use them to keep simple calls simple
Keyword arguments (enthusiasm=5) make calls self-documenting

Return vs. print — the classic beginner mix-up

def add_broken(a, b):
    print(a + b)           # shows the answer — but returns None

def add_good(a, b):
    return a + b           # gives the answer back to the caller

result = add_broken(2, 3)  # prints 5, but result is None
total = add_good(2, 3)     # total is 5 — usable

print(result * 2)          # TypeError: NoneType
print(total * 2)           # 10If a function needs to be usable in another function, it must return, not print.

Mini-exercise: price calculator

Write calc_total(items: list[float], tax_rate: float = 0.08) -> float
Return the sum of items times (1 + tax_rate), rounded to 2 decimals
Add a docstring
Call it with [3.50, 4.25, 1.99] and print the result
Ask your AI assistant to review your code and suggest improvements

Loose function	Tight function
def do(x)	def fetch_user(user_id: int) -> dict
No docstring	Docstring with Args and Returns
Prints results	Returns results
Hard to test	Easy to test

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-prog-python-functions-builders

What is the core idea behind "Python Functions — Writing Your Own Tools"?
1. A function is a reusable chunk of code with a name. You'll write three, add type hints, and let AI suggest better names and docstrings.
2. An agent is a loop: model decides, tool runs, model reads result, decides again.
3. f-string
4. Async lets your program make 100 API calls at once instead of one at a time.
Which term best describes a foundational idea in "Python Functions — Writing Your Own Tools"?
1. parameter
2. function
3. return
4. type hint
A learner studying Python Functions — Writing Your Own Tools would need to understand which concept?
1. function
2. return
3. parameter
4. type hint
Which of these is directly relevant to Python Functions — Writing Your Own Tools?
1. function
2. parameter
3. type hint
4. return
Which of the following is a key point about Python Functions — Writing Your Own Tools?
1. A parameter is the name in the definition (name, enthusiasm)
2. An argument is the value you pass when calling ("Maya", 5)
3. Defaults let callers skip arguments — use them to keep simple calls simple
4. Keyword arguments (enthusiasm=5) make calls self-documenting
Which of these does NOT belong in a discussion of Python Functions — Writing Your Own Tools?
1. Defaults let callers skip arguments — use them to keep simple calls simple
2. An argument is the value you pass when calling ("Maya", 5)
3. An agent is a loop: model decides, tool runs, model reads result, decides again.
4. A parameter is the name in the definition (name, enthusiasm)
Which statement is accurate regarding Python Functions — Writing Your Own Tools?
1. Return the sum of items times (1 + tax_rate), rounded to 2 decimals
2. Add a docstring
3. Write calc_total(items: list[float], tax_rate: float = 0.08) -> float
4. Call it with [3.50, 4.25, 1.99] and print the result
Which of these does NOT belong in a discussion of Python Functions — Writing Your Own Tools?
1. Add a docstring
2. Return the sum of items times (1 + tax_rate), rounded to 2 decimals
3. An agent is a loop: model decides, tool runs, model reads result, decides again.
4. Write calc_total(items: list[float], tax_rate: float = 0.08) -> float
What is the key insight about "Docstrings make AI smarter" in the context of Python Functions — Writing Your Own Tools?
1. A docstring (the triple-quoted string at the top of a function) tells both humans and AI what the function does.
2. An agent is a loop: model decides, tool runs, model reads result, decides again.
3. f-string
4. Async lets your program make 100 API calls at once instead of one at a time.
What is the recommended tip about "Review before you run" in the context of Python Functions — Writing Your Own Tools?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. AI code is a first draft. Read through it, understand each line, and test on sample inputs before merging into real proj…
3. f-string
4. Async lets your program make 100 API calls at once instead of one at a time.
Which statement accurately describes an aspect of Python Functions — Writing Your Own Tools?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. f-string
3. A function packages logic so you can call it by name anywhere. Name it clearly and your code reads like a story.
4. Async lets your program make 100 API calls at once instead of one at a time.
What does working with Python Functions — Writing Your Own Tools typically involve?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. f-string
3. Async lets your program make 100 API calls at once instead of one at a time.
4. Big idea: a function is a small contract. Its name says what it does, its signature says how to use it, its docstring says why.
Which best describes the scope of "Python Functions — Writing Your Own Tools"?
1. It focuses on A function is a reusable chunk of code with a name. You'll write three, add type hints, and let AI s
2. It is unrelated to ai-coding workflows
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about Python Functions — Writing Your Own Tools?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. Parameters, arguments, defaults
3. f-string
4. Async lets your program make 100 API calls at once instead of one at a time.
Which section heading best belongs in a lesson about Python Functions — Writing Your Own Tools?
1. An agent is a loop: model decides, tool runs, model reads result, decides again.
2. f-string
3. Return vs. print — the classic beginner mix-up
4. Async lets your program make 100 API calls at once instead of one at a time.

← Back to interactive lesson