AI Code Comment Generator (Free Tool)
AI-powered comment and documentation generator for Python, JavaScript, Java, C++, and 100+ languages. Generate function docstrings, JSDoc, Javadoc, inline comments, and comprehensive documentation following language-specific standards.
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Issue Description
Hint: Paste your code and click Comment to auto-generate clear, professional inline comments and documentation.
How It Works
- 1
Paste Your Code
Copy uncommented functions, classes, methods, or entire modules from any programming language. Supports Python, JavaScript, TypeScript, Java, C++, C#, Go, PHP, Ruby, and 100+ languages.
- 2
AI Analyzes Code Purpose
Advanced AI analyzes function purpose, parameter usage, return values, side effects, exceptions, algorithm complexity, edge cases, and dependencies to generate accurate documentation.
- 3
Get Formatted Documentation
Receive language-specific documentation in proper format: Python docstrings (Google/NumPy style), JSDoc for JavaScript, Javadoc for Java, inline comments for complex logic, and usage examples.
Manual vs AI Documentation
| Feature | Manual Comments | AI-Generated Docs |
|---|---|---|
| Documentation | Manual Writing | AI-Generated |
| Speed | Minutes per function | Seconds per function |
| Consistency | Varies by developer | Standardized format |
| Coverage | Often incomplete | Comprehensive details |
| Standards | May not follow | Follows style guides |
| Maintenance | Time-consuming | Quick regeneration |
Examples
Example 1: Python Docstring Generation
def calculate_fibonacci(n, memo=None):
if memo is None:
memo = {}
if n in memo:
return memo[n]
if n <= 1:
return n
memo[n] = calculate_fibonacci(n-1, memo) + calculate_fibonacci(n-2, memo)
return memo[n] def calculate_fibonacci(n: int, memo: dict = None) -> int:
"""
Calculate the nth Fibonacci number using memoization.
Uses dynamic programming with memoization to efficiently compute
Fibonacci numbers. The memo dictionary caches previously computed
values to avoid redundant recursive calls.
Args:
n (int): The position in Fibonacci sequence (0-indexed).
memo (dict, optional): Cache for memoization. Defaults to None.
Returns:
int: The nth Fibonacci number.
Raises:
ValueError: If n is negative.
Examples:
>>> calculate_fibonacci(5)
5
>>> calculate_fibonacci(10)
55
Time Complexity: O(n)
Space Complexity: O(n)
"""
if memo is None:
memo = {}
if n in memo:
return memo[n]
if n <= 1:
return n
memo[n] = calculate_fibonacci(n-1, memo) + calculate_fibonacci(n-2, memo)
return memo[n] Key Changes:
The AI generated comprehensive Python docstring following Google style guide. It identified the function purpose (Fibonacci calculation with memoization), documented parameters with type hints, explained the algorithm (dynamic programming), included return value documentation, added usage examples, provided complexity analysis (O(n) time and space), and suggested potential exception handling. The docstring explains why memoization is used and how it improves performance from O(2^n) to O(n). This helps developers understand the algorithm without reading implementation details.
Example 2: JSDoc Generation for JavaScript
async function fetchUserData(userId, includeOrders = false) {
const response = await fetch(`/api/users/${userId}`);
if (!response.ok) throw new Error('User not found');
const user = await response.json();
if (includeOrders) {
user.orders = await fetchOrders(userId);
}
return user;
} /**
* Fetches user data from the API with optional order history.
*
* Makes an HTTP GET request to retrieve user information by ID.
* Optionally fetches and includes the user's order history.
*
* @async
* @param {string|number} userId - The unique identifier of the user
* @param {boolean} [includeOrders=false] - Whether to fetch user orders
* @returns {Promise<Object>} User object with personal data and optional orders
* @returns {string} returns.id - User ID
* @returns {string} returns.name - User full name
* @returns {string} returns.email - User email address
* @returns {Array<Object>} [returns.orders] - User orders if requested
* @throws {Error} When user is not found (HTTP 404)
* @throws {Error} When network request fails
*
* @example
* // Fetch basic user data
* const user = await fetchUserData('123');
*
* @example
* // Fetch user with order history
* const userWithOrders = await fetchUserData('123', true);
*/
async function fetchUserData(userId, includeOrders = false) {
const response = await fetch(`/api/users/${userId}`);
if (!response.ok) throw new Error('User not found');
const user = await response.json();
if (includeOrders) {
user.orders = await fetchOrders(userId);
}
return user;
} Key Changes:
The AI generated complete JSDoc following JSDoc 3 specification. It documented the async nature (@async), parameter types with TypeScript-style syntax (string|number for userId), optional parameters with defaults ([includeOrders=false]), Promise return type with detailed object structure, multiple @throws tags for different error cases, and practical usage examples showing both basic and advanced usage. The documentation explains the conditional order fetching logic and helps IDEs provide better autocomplete and type checking. This makes the API easier to use and maintains clear documentation standards across JavaScript projects.
Frequently Asked Questions
The AI generates language-specific documentation: Python docstrings (Google/NumPy/Sphinx style), JSDoc for JavaScript/TypeScript, Javadoc for Java, XML comments for C#, Doxygen for C++, inline comments explaining complex logic, parameter descriptions, return value documentation, exception/error documentation, usage examples, and time/space complexity analysis for algorithms.
Yes. The AI follows official documentation standards for each language: PEP 257 for Python docstrings, JSDoc 3 specification for JavaScript, Javadoc conventions for Java, TSDoc for TypeScript, Doxygen for C/C++, XML documentation for C#, RDoc for Ruby, and GoDoc for Go. It includes proper formatting, parameter types, return values, exceptions, and examples.
Yes. The AI analyzes existing uncommented code and generates comprehensive documentation. It identifies function purpose, parameters, return values, side effects, algorithm complexity, edge cases, and dependencies. Perfect for documenting legacy code, improving code reviews, and maintaining clean documentation standards across teams.