Code documentation writer: how to refine generated text to precise function descriptions
The Code documentation writer tool creates a draft description of provided functions: parameters, return values, examples and limitations. The first result almost always requires refinement because the model does not know your project context. This guide shows how to transform generated text into accurate and useful documentation over several iterations, without adding non-existent capabilities.
Open tool: Code documentation writer ↗What the tool does and what input it needs
The tool accepts two fields: "Code and error message" (up to 8000 characters) and "Platform or application" (up to 1000 characters). Into the first field you insert the source code of functions that need documentation. The second field clarifies the environment: language, framework, version, environment specifics. Output is a single text result in a separate panel, which can be copied or downloaded as TXT.
Important to understand: the tool does not analyze your repository, does not connect to the IDE and does not check code for execution. It works only with the text you provide. If there are obscure dependencies or side effects in the code, they must be described in the platform field or added to code comments.
For example, for a Python function it is sufficient to pass its definition. But if the function uses a global variable or external API, indicate this in the platform description, otherwise the model may misinterpret the behavior.
Field "Code and error message": def add(a, b): return a + b. Field "Platform or application": Python 3.11, pure function with no side effects.
First result: what usually comes out and what to check
After pressing "Generate" you will receive structured text: function description, list of parameters with types, description of the returned value, usage example and a limitations section. This is a good foundation, but it may contain inaccuracies: the model may assign the wrong type to a parameter, add a non-existent exception or suggest an example that does not match your codebase.
Check every item. Pay special attention to limitations: the model often writes general phrases like "does not work with negative numbers" if the code has no explicit check. Such statements need to be either confirmed or removed. Also check that the code example is syntactically correct and uses only functions that really exist in your project.
Do not try to fix everything at once. First mark factual errors, then stylistic rough edges. This will help formulate precise edits for the next iteration.
First result may contain: "Parameter a: integer". If the code has no annotations, this is an assumption, not a fact. Check against the source.
How to formulate edits for the second iteration
The second iteration is a repeated run with refined input. You do not need to rewrite the whole code again. It is enough to add brief instructions to the "Platform or application" field: what types to use, which exceptions to mention, which examples to provide. For example: "Parameters - integers. Returns integer. Does not handle overflow. Example: add(2, 3) returns 5".
If the first result has extra sections, indicate this in the platform description: "Do not add a performance section, only parameters, return, example and limitations". The tool has no settings for selecting sections, so management is done via text request.
Formulate edits specifically. Instead of "make better" write "replace example with a call with two arguments and expected result". The more precise the instruction, the closer the second result is to ready-to-use text.
Add to the platform field: "Types: a and b - integers. Return: integer. Limitation: does not check overflow. Example: add(2, 3) -> 5".
Checking examples and limitations for realism
Examples must be executable and correspond to the function signature. If the first result example calls the function with a different number of arguments or uses a non-existent module, this must be corrected. In the second iteration explicitly specify expected output: "add(2, 3) returns 5". The model does not run code, so it may err in calculations if the example is complex.
Limitations - the most vulnerable part. The model tends to invent limitations that are not in the code. Check every statement: does the function really not handle negative numbers? Is there a None check? If the code has no explicit handling, it is better to write "behavior with negative values is not defined" rather than asserting it does not accept them.
If the function returns None in some cases, this is also a limitation. Make sure it is reflected. Do not add limitations that do not follow from the code or platform description.
For def add(a, b): return a + b correct limitation: "Does not perform type checking; passing strings will result in concatenation". This follows from Python behavior.
Working with the error message in the input field
The "Code and error message" field allows passing not only code but also error text. This is useful if the function raises an exception under certain conditions. For example, if a function divides by zero, add the message "ZeroDivisionError: division by zero". Then the model can describe this exception in the limitations section.
However do not overload the field: the limit is 8000 characters. If the code is large, it is better to split it into several runs per function. The result will be more accurate and verification easier. Create a separate request for each function.
If the error does not relate to the function being documented, do not include it. The model may erroneously link the message to the wrong part of the code.
Add to the code field: "If b=0 raises ZeroDivisionError". This will help generate a correct limitation.
Final proofreading and tool limitations
After the second or third iteration you have a draft that needs manual proofreading. Check: are all parameters described, does the returned value match the code, are there invented exceptions, is the example correct. The tool does not guarantee error-free output, so final responsibility lies with you.
Remember technical limitations: no DOCX or PDF export, no batch processing, no automatic saving. Result can only be copied or downloaded as TXT. Vector images (SVG, PNG) and speech (eSpeak WAV) are available but do not relate to this task. The model may err, and queue and availability vary.
Do not use the tool for generating documentation of closed source code if this violates security policy. Also do not expect it to replace manual code testing or analysis.
Final text for add(a, b): "Function add takes two integers and returns their sum. Parameters: a, b - integers. Return: integer. Example: add(2, 3) -> 5. Limitation: does not check types; passing strings performs concatenation".
Refining documentation from code is an iterative process: generate a draft, check facts, refine input and repeat. The Code documentation writer tool speeds up creating a base, but does not replace careful analysis. Following the steps described will give you an accurate function description with realistic examples and limitations, without adding non-existent capabilities.
Open tool: Code documentation writer ↗Interface translations are machine-assisted and may need refinement.