This is a submission for the GitHub Finish-Up-A-Thon Challenge
AutoDoc is a tool that automatically generates enriched OpenAPI 3.0.3 documentation from raw C# ASP.NET Core controller code using a local AI model - no manual annotations, no attribute decorators, no maintenance required.
The idea came from a real frustration I noticed during my software engineering internship. Swagger documentation in .NET projects only documents what developers explicitly declare. Miss an attribute, forget an error response, skip a summary and your docs silently drift from reality. Developers spend time writing documentation instead of writing code.
AutoDoc fixes this by reading raw C# controller code directly and inferring everything automatically: routes, HTTP methods, parameters, response codes, error schemas, and operation summaries.
The project was built in 3 phases:
| Phase | What it does |
|---|---|
| Phase 1 - Console App | Paste controller code, get OpenAPI YAML in the terminal |
| Phase 2 - Docker Web API | Containerised REST backend, call via HTTP POST, get YAML back |
| Phase 3 - Playground UI | Browser dashboard: paste code, see Raw YAML + Swagger Preview instantly |
What started as a terminal script became a fully containerised, browser-accessible API documentation tool and this challenge is what pushed me to finish it.
How it works: paste any C# ASP.NET Core controller, click generate, get full OpenAPI docs instantly.
The AutoDoc Playground UI is a split-panel dashboard. On the left, you paste any ASP.NET Core controller code - no modifications needed. The sample TodoController
comes pre-filled so you can test immediately. Hit Generate OpenAPI Docs and AutoDoc sends the raw code to the AI backend running locally via Ollama.No attributes. No decorators. No [ProducesResponseType]
. Just raw C# code.
Input: this is the only code AutoDoc received:
[ApiController]
[Route("api/[controller]")]
public class TodoController : ControllerBase
{
[HttpGet]
public IActionResult GetAll() => Ok(new[] { "Task 1", "Task 2" });
}
No [ProducesResponseType]
. No [SwaggerOperation]
. No XML comments.
No manual annotations of any kind. Just raw C# code.
Output: full OpenAPI 3.0.3 spec generated automatically:
AI inferred everything automatically:
| What was inferred | How |
|---|---|
operationId |
|
Unique names like GetAllTodos , CreateTodo |
|
tags |
|
Grouped by controller name TodoController |
|
summary |
|
| Human-readable descriptions per endpoint | |
requestBody |
|
| Schema inferred from method parameters | |
responses |
|
| 200, 201, 400, 404, 500 from code logic | |
components/schemas/ErrorResponse |
|
| Defined once, referenced everywhere |
none of this existed in the original controller code.
Switching to the Swagger Preview tab renders the YAML as a live interactive Swagger UI. The POST /api/Todo
endpoint shows:
The GET /api/Todo
endpoint shows:
Ok(new[] { "Task 1", "Task 2" })
.
Internal Server Error fallback on every endpoint automatically.The AI understood what Ok(new[] { ... })
returns and documented it correctly as an array schema.
At the bottom of the Swagger Preview, the Schemas section shows the auto-generated ErrorResponse
schema with:
code
: integermessage
: stringThis schema was never defined anywhere in the C# controller. AutoDoc inferred it from the pattern of error responses across all endpoints and generated it once, referenced consistently throughout the entire spec.
AutoDoc began as a simple console application - a proof of concept to answer 1 question:
Can an AI model read raw C# controller code and generate valid OpenAPI documentation without any manual annotations?
The answer was yes. But the Phase 1 console app was far from finished:
| What existed | What was missing |
|---|---|
| Console app that called Ollama locally | No HTTP interface - unusable by others |
| Raw YAML printed to terminal | No output validation |
| Basic Llama 3.2 prompt | No error handling |
| Proof the concept worked | No UI, no Docker, no way to share it |
It worked on my machine. That was it.
Phase 2 - From console app to Docker REST API
The console app was converted into a proper ASP.NET Core minimal API with 2 endpoints:
POST /generate-openapi
-
receives controller code as JSON, returns enriched OpenAPI YAML.
GET /health -
confirms the service is running.A
Dockerfile
was added so the entire service runs in a container with a single docker run
command. The Ollama host was made configurable via environment variable so it works both locally (localhost:11434
) and inside Docker (host.docker.internal:11434
).
Phase 3 - Playground UI
A browser-based dashboard was built and served directly from the Docker container via wwwroot
. No separate frontend deployment needed - the UI and API ship together.
The playground features:
The hardest part - taming Llama 3.2 output
Llama 3.2 is a small, free, local model. It does a remarkable job generating mostly correct OpenAPI YAML - but it makes small inconsistent mistakes. Each one broke the Swagger Preview with a parsing error.
A full post-processing pipeline was built to fix every category of output error:
| Helper function | Problem it solves |
|---|---|
MergeDuplicatePaths |
|
| Llama repeating the same API path twice | |
MergeDuplicateMethods |
|
| Llama repeating GET/POST under the same path | |
MergeDuplicateComponents |
|
Llama emitting components: key twice |
|
RemoveDuplicateSchemaKeys |
|
Llama defining ErrorResponse twice |
|
RemoveMalformedSecurity |
|
Llama adding broken security: blocks |
|
FixMalformedOperationIds |
|
Llama using {verb} as a literal placeholder |
|
NormaliseOutput |
|
Schema name typos like ErrorRespond |
|
Each helper was born from a real parsing error seen in production output. The pipeline runs on every generation before the YAML is returned to the client.
| Phase 1 - Console App | Phase 3 - Full AutoDoc | |
|---|---|---|
| Interface | Terminal only | Browser dashboard |
| Deployment | Local .NET required | Docker container |
| Output | Raw YAML in terminal | Raw YAML + live Swagger Preview |
| Validation | None | YAML cleaning pipeline + spec validation |
| Usability | Developer only | Anyone with a browser |
| Shareable | No | Yes - 1 docker run command |
What started as a terminal experiment is now a containerised, browser-accessible documentation tool that anyone can run and use.
GitHub Copilot was my implementation partner throughout AutoDoc.I defined the problems and architecture,Copilot wrote the code.
I used Copilot not for autocomplete but as a problem-to-code translator. I described what I needed in plain English, Copilot read my existing code, and produced working implementations I could review, test, and keep.
The first key prompt connected the backend to the frontend:
"I am building an ASP.NET Core 10 Minimal API called AutoDoc.I want to serve static files from a wwwroot folder and make sure it serves index.html as the default page. How should I update my Program.cs to allow this?"
Copilot read my existing Program.cs
directly, understood the current pipeline, and knew exactly what to add without breaking anything.
It explained what it was doing and why - then applied the change automatically:
app.UseDefaultFiles();
app.UseStaticFiles();
2 lines. Copilot explained that UseDefaultFiles()
rewrites /
to /index.html
and UseStaticFiles()
serves everything in wwwroot
. It then validated the file for compile errors before confirming. The change was applied in one shot - I clicked Keep.
The result: index.html
created in wwwroot
, Program.cs
updated, Copilot confirming 1 file changed, +4 -0. The Playground UI was now being served directly from the Docker container.
With the static file serving in place, the next prompt built the entire frontend:
"Build a dark-themed single-page HTML dashboard for AutoDoc.Left panel: controller name input and C# code textarea with a Generate button. Right panel: tabbed output with Raw YAML (monospace) and Swagger Preview rendered from the YAML using SwaggerUIBundle. Show a status pill in the header that updates during generation. No frameworks, vanilla JS only."
Copilot generated the complete index.html
:
fetch
call to /generate-openapi
with full error handling.jsyaml
for YAML-to-spec parsing.The entire Playground UI came from a single prompt.
Every YAML cleaner in AutoDoc came from describing a real Llama output bug to Copilot:
"Llama is generating thecomponents:
key twice in the output YAML. Write a C# helper that scans line by line and removes duplicatecomponents:
keys, keeping only the first."
Copilot wrote MergeDuplicateComponents
. I repeated this pattern for all seven helpers - each one targeting a specific category of Llama output error discovered during testing.
Copilot was most valuable not when I said "write this" but when I said "here is the problem I am seeing, fix it."
It read my existing code before responding. It explained its reasoning. It validated changes before applying them. It felt less like autocomplete and more like a senior developer who already knew my codebase.
Copilot is powerful but not perfect. Here is what it could not do:
| Limitation | What happened |
|---|---|
| Could not predict Llama output bugs | Every YAML cleaner was reactive - I discovered the bug first, then asked Copilot to fix it |
| Did not flag environment differences | Generated host.docker.internal hardcoded - broke local dotnet run , I found the error myself |
| Could not test browser output | Generated the Playground UI confidently but could not see the broken Swagger Preview in a browser |
| Prompt quality = output quality | Vague prompts gave generic results - writing precise prompts was a skill I had to develop |
The pattern was consistent: Copilot excelled at implementation, but discovery and debugging required a human.It could not run the code, open the browser, or experience the errors - I had to do that and bring the findings back to Copilot to fix.
AutoDoc is functional but it is just the beginning.
The current version uses Llama 3.2 locally via Ollama which is free
and private, but limited in output consistency. The natural next step is upgrading to a more capable model for cleaner, more reliable YAML generation.
Here is what I hope to build next:
| Feature | Why |
|---|---|
| Support for larger Ollama models (Llama 3.1, Mistral) | Better output quality, fewer post-processing fixes needed |
| Multi-controller batch generation | Generate docs for an entire project at once |
| GitHub Actions CI/CD integration | Auto-generate docs on every push |
| Diff-based incremental generation | Only regenerate endpoints that changed |
| Export to JSON | Support both YAML and JSON OpenAPI formats |
The biggest hope is this: AI-generated documentation that stays in sync with code automatically - no manual maintenance, no drift, no outdated Swagger specs.
Developers should write code. The AI should write the docs.
Working with GitHub Copilot on AutoDoc changed how I think about
building software. Before this project, I used it for small autocomplete suggestions. After this project, I use it as a genuine collaborator.
What I hope to do differently next time:
The most important thing I learned: Copilot is only as good as the problem you give it.vague problems get vague code. Clear problems get working code.
AutoDoc taught me to think more clearly about problems because I had to describe them precisely enough for an AI to solve them. That skill makes me a better developer with or without Copilot.
Try it yourself! clone the repo, run Ollama, and paste your 1st C# controller. I'd love to see what AutoDoc generates for your API. Drop a comment with your results.