Code documentation writer: how to document functions with AI
Writing code documentation is often postponed because it is routine and requires context switching. The Code documentation writer service takes over the rough generation of function descriptions: you paste code, select the output language and get a text file with parameters, return values, examples and limitations. But it is not a replacement for an engineer: AI does not know business logic, does not see the whole project and may make mistakes in details. In this article we will split the work between human and service, check mandatory points and cases where it is better to write documentation yourself.
Open tool: Code documentation writer ↗What the service does and what remains with you
The service accepts up to 8000 characters of code in the first field and up to 1000 characters of additional details in the second. You select the output language, click Generate and receive one result in a separate panel. You can copy it or download it as a plain text TXT file. There are no style presets, sliders or detail levels in the interface — this deliberate limitation simplifies work but requires you to provide clearer input code.
AI handles the mechanical part well: it lists parameters, indicates types if they are in the signature, suggests a call example and describes the return value. It can also formulate limitations if they are visible in the code: for example, a function does not handle None or requires a positive number. But it does not know why this function is needed in your product, which side effects are important for the team and which project conventions are in place.
Therefore manual work remains with you: check the meaning, add context, remove fabricated details and bring the style in line with the common standard. The service is a draft accelerator, not the author of the final document.
Input: def add(a, b): return a + b. Service output may look like: 'The function add takes two numbers a and b and returns their sum. Parameters: a — first addend, b — second addend. Returns: sum of a and b. Example: add(2, 3) will return 5. Limitations: does not check types, with string input it performs concatenation.'
How to prepare code so the result is useful
The service sees only what you paste. If you send just one line def add(a, b): return a + b, it will not know that a and b are numbers, not strings. Add a note in the second field: 'a and b are integers, the function is used in a calculator'. This will not change the code but will help AI formulate limitations and examples more accurately.
Break large files into logical blocks. The 8000 character limit allows several functions to be included, but if there are too many, descriptions become superficial. Better to send 3–5 related functions and indicate how they interact. If a function depends on a global variable or external API, write about it in the details — otherwise AI may invent non-existent behavior.
Remove unnecessary from the insertion: commented code, debug prints, long meaningless strings. Cleaner input means less noise in output. If the code has non-standard names, decode them in the second field.
Instead of sending the whole module send: def calculate_discount(price, percent): if percent < 0 or percent > 100: raise ValueError; return price * (1 - percent / 100). In the second field indicate: 'price — price in rubles, percent — discount in percent, function does not accept negative values.'
What to check in the generated text
First — correspondence with the signature. AI may mix up parameter order, add a non-existent argument or name the return value differently. Check every item against the code. Second — examples. Make sure the example will actually execute and give the described result. If the code has type checking or exception raising, the example should account for that.
Third — limitations. AI often writes generic phrases like 'may cause an error with incorrect data'. This is not useful. Replace with specifics: 'If percent is more than 100, ValueError is raised'. Fourth — style. If your team uses tables to describe parameters or certain terms, bring the text to this style manually.
Fifth — absence of fabricated details. AI does not know your architecture and may attribute database work to a function that is not there. Always verify that no extra entities appeared in the description.
AI may write: 'Function add returns a string'. Check: in the code return a + b, and if a and b are numbers, the result is a number. Fix to 'returns the sum of numbers.'
When the service is not suitable
If a function is tightly connected with business rules not visible in the code, AI will not be able to describe them. For example, tax calculation depends on region and client status — this data comes from outside. In such cases it is better to write documentation yourself or use the service only for a draft, then add context.
If the code contains secrets, tokens or personal data, do not paste it into the web form. The service is not intended for confidential information. Also it is not suitable for documenting entire modules with hundreds of functions: the 8000 character limit and single result do not allow covering a large volume.
Do not expect the service to create documentation in DOCX or PDF format, perform batch processing or automatically save the result. Export is only TXT, images — SVG and PNG, speech — WAV, subtitles — SRT. If you need another format, you will have to convert manually.
Function def get_user_data(user_id): accesses a closed API with a key. It is not advisable to insert its code into the service. Describe the behavior manually, and ask AI to clarify only the parameter phrasing.
Practical scenario: documenting three functions
Suppose you have three functions: add(a, b), subtract(a, b) and multiply(a, b). You paste them into the first field, in the second write: 'All functions take integers and return an integer. Used in a calculator.' You select Russian output language and click Generate.
The service will return text with a description of each function. You check: for add the return is sum, for subtract — difference, for multiply — product. Examples must be correct: add(2, 3) → 5, subtract(5, 2) → 3, multiply(4, 3) → 12. If AI wrote that multiply returns a string, you fix it.
Then you add limitations: 'Functions do not check argument types. With string input multiply repeats the string several times'. This important clarification AI might not output on its own. Ready text copy to your repository or download as TXT.
Input: def add(a, b): return a + b; def subtract(a, b): return a - b; def multiply(a, b): return a * b. Output after checking: 'add(a, b) — returns sum. Example: add(2, 3) = 5. Limitation: does not check types.'
Service limitations and typical errors
The service does not guarantee faultlessness. The model may make mistakes, and queue and availability vary. Do not treat the result as truth. Always verify against the code. Another error — trying to describe too much in the second field: the 1000 character limit. Better to sacrifice secondary details than get truncated input.
Do not use the service to generate documentation for code you do not understand. If you cannot verify the result, AI may fix an error. Also useless to ask it to describe project architecture: it sees only a fragment.
Remember that the service does not execute code. Examples it proposes may be incorrect from the point of view of syntax or logic. Check them in your environment.
AI may write: 'Function returns None if arguments are not provided'. But in the code there is no handling of missing arguments — an error will occur. Fix the description.
Code documentation writer service saves time on routine drafting: it lists parameters, suggests examples and formulates limitations. But final responsibility lies with a human. Check every item, add business context, remove fabrications and watch the style. If the code is confidential or logic is more complex than the signature, write documentation yourself. Use AI as an assistant, not as an author. Then documentation will be accurate and useful for the team.
Open tool: Code documentation writer ↗Interface translations are machine-assisted and may need refinement.