Code documentation writer: how to get accurate function descriptions
The Code documentation writer tool creates descriptions of provided functions: parameters, return values, examples and limitations. It analyzes code text without executing it, so the result depends on the completeness and clarity of the input data. In this article we’ll explore how to prepare code for generation, what to expect from the output text and how to verify it against the source. All examples in this article are fictional illustrations, not real tool outputs.
Open tool: Code documentation writer ↗What the tool actually analyzes
"Code documentation writer" accepts function text and generates a description based on its signature, body and comments. The tool does not run code, does not check types at runtime and does not test behavior. If there is branching inside a function, it will only be reflected if it is explicitly read in the text. Therefore documentation always remains a draft that must be cross-checked with the source.
Input fields are limited: up to 8000 characters for code and error message and up to 1000 characters for platform or application. This means long modules or multiple functions at once may not fit. It is better to document one function at a time, and context — language version, library, purpose — should be entered in the separate field.
The tool does not ask clarifying questions. Everything you want in the description must be explicitly provided in the input data: comments about types, limitations, side effects. The model knows nothing about your project beyond the text provided.
Input: Python 3.9, function that adds two numbers. def add(a, b): return a + b
Preparing code: what increases accuracy
Syntax-correct code with proper indentation and closing brackets is read by the model more predictably. If a fragment is truncated or contains errors, the description may become distorted. It is useful to verify before generation that the function compiles at least syntactically.
Comments with types and limitations often end up in the final output. For example, a line like # type: (int, int) -> int or # raises ValueError if b == 0 helps the model include corresponding points in the description. Without such hints types and exceptions may be omitted.
If a function uses external libraries, global variables or specific project conventions, add a brief explanation. Specify the platform, language version and intended use in the Platform or application field. This reduces the risk of incorrect assumptions about the function's purpose.
Poor input: def process(data): ... Good input: Python 3.9, pandas DataFrame. # type: (pd.DataFrame) -> pd.Series. def process(data): ...
Selecting output language and formulating the request
Before generation select the output language. If Russian text is needed, check this parameter: by default the result may be in English. Mixing languages in code comments can also lead to confusion in the description.
Formulate the request specifically. Instead of 'make documentation' specify exactly what needs to be described: parameters, return value, call example, limitations. The tool is oriented at code analysis, so complex instructions should not be overloaded.
Do not expect the model to guess the function's purpose from a general name like process_data or handle_request. Add a comment with business logic or specify the intended use in the Platform field.
Request: 'Describe the add function: parameters a and b, return value, call example. Output language: Russian.'
How to verify the generated description
Compare every point of the description with the source code. Check that all parameters are listed, types match, and the return value corresponds to actual behavior. If a function returns None in some cases but the text states otherwise, that is an error.
Pay attention to examples. The tool does not execute code, so examples may be plausible but incorrect. They need to be checked separately. If an example contradicts the function's logic, correct it manually or refine the input data.
If the description turned out too general, add context: comments about purpose, limitations, side effects. Re-generation with refined input often gives a more specific result. But final verification always remains with the developer.
Generated: 'add(a, b) returns the sum of a and b'. Verification: if the code has no type checking, add a limitation manually.
Typical errors and how to fix them
Incomplete or incorrect code is the most common reason for inaccurate description. If a fragment is truncated, the model may misinterpret the function's purpose. Fix: pass a syntactically correct fragment and add missing explanations.
Lack of context leads to incorrect assumptions about types and intended use. Fix: specify platform, language version and libraries in the appropriate field, add comments about external dependencies.
A too large fragment with multiple functions may confuse the model. Fix: document one function at a time or split code into logical blocks. Remember the 8000-character limit for the first field.
Instead of one large fragment with three functions, pass three separate requests with brief context for each.
Tool limitations and what to remember
The tool does not execute code, does not test it and does not guarantee that the description matches actual behavior. Complex constructs — decorators, generators, async functions — may be reflected simplistically if not explicitly described. Add explanations in the input field.
Each request is processed separately: generation history is not saved, batch processing is not provided. If you need to document many functions, the process must be repeated. Model responses may contain errors, and service availability may vary.
Do not rely on the tool as the sole source of documentation. It accelerates draft creation but does not replace manual verification. For public API make sure no internal implementation details ended up in the text.
If a function uses @lru_cache, add a comment # with caching so the model mentions it in the description.
Practical example: from code to description
Take a fictional Python function: def add(a, b): return a + b. Without context the tool may create a basic description: 'The add function takes parameters a and b and returns their sum'. This is correct but incomplete.
If you specify the platform 'Python 3.9, mathematical library' and add a comment # type: (int, int) -> int, the description may become more specific: 'add(a, b) returns the sum of two numbers. Parameters: a, b — integers. Returns: integer. Example: add(2, 3) -> 5. Limitation: no type checking'.
Such text can be used as a draft. Before publication cross-check it with the code, verify the example and edit manually if needed. The tool provides a starting point, not a final document.
Input: Python 3.9, mathematical library. # type: (int, int) -> int. def add(a, b): return a + b. Output: 'add(a, b) — returns the sum of a and b. Parameters: a, b — integers. Returns: integer. Example: add(2, 3) = 5. Limitation: no type checking.'
Code documentation writer helps quickly prepare a function description draft, but requires careful preparation of input data and mandatory result verification. Main errors are related to incomplete code, lack of context and ambiguous requirements. Specify platform, add comments about types and limitations, document one function at a time. Remember that the tool does not execute code and does not guarantee error-freeness: final cross-check with the source remains with you. Such an approach makes generation more predictable and saves time on routine description.
Open tool: Code documentation writer ↗Interface translations are machine-assisted and may need refinement.