# Overleaf Direct MCP

A self-hosted MCP server for synchronizing LaTeX answers into Overleaf cloud projects. Everyday operations send direct network requests and do not drive a browser.

This is an UNOFFICIAL integration using overleaf-sync and Overleaf web endpoints. It is not affiliated with Overleaf or OpenAI. Endpoints may change.

## Tools

- overleaf_status: local session and sync dependency diagnostics.
- list_projects: list projects from the Overleaf cloud account.
- list_files: list LaTeX files in a selected project.
- read_file: fetch current source as file_content, sha256, and answer_slots.
- update_answer: update a selected answer environment with SHA-256 check, backup, cloud write and readback verification.
- compile_and_download: request Overleaf cloud compilation and save PDF with project URL and engine diagnostics.

The AI assistant reads handwriting and produces LaTeX. This server handles authenticated Overleaf synchronization rather than OCR.

## Requirements and setup

Python 3.11+, an Overleaf account, and the dependencies in requirements.txt. The supplied one-click setup scripts target Windows.

    py -3 -m venv .venv
    .\.venv\Scripts\python.exe -m pip install -r requirements.txt
    .\LOGIN_OVERLEAF.cmd

Sign in locally when the login helper prompts. It stores a private session at .state/.olauth, which Git ignores. Never share this session or tunnel keys.

For a stdio MCP client, run the repository's server.py with the Python executable from its virtual environment.

To connect a ChatGPT MCP Tunnel, obtain the official OpenAI tunnel-client binary using:

    .\.venv\Scripts\python.exe scripts\install_tunnel_client.py

Create your OWN dedicated Tunnel through OpenAI Platform, associate your ChatGPT workspace, create a runtime key with the appropriate Tunnel permissions, then run SETUP_OVERLEAF_TUNNEL.cmd. The secret key is DPAPI-encrypted for the current Windows account. Start the service with RUN_OVERLEAF_TUNNEL.cmd. Do not reuse another person's tunnel ID or private profiles.

## Safe editing workflow

1. Call list_projects, list_files and read_file. Confirm the project and source text.
2. Match the intended question to the answer slot, which counts answer environments from 1 (not textbook exercise numbers).
3. Prepare LaTeX from the source material and call update_answer with the latest expected_sha256.
4. Read back the document from Overleaf, then compile_and_download and inspect the PDF.

The read_file text field is named file_content, to avoid a naming collision with some ChatGPT MCP result envelopes. The upstream upload mechanism still has a brief revision-check-to-upload race; avoid simultaneous edits by others.

## Tests

    .\.venv\Scripts\python.exe -m unittest discover -s tests -v

Optional cloud acceptance scripts are in scripts/. They create or modify a DEDICATED test project on your logged-in Overleaf account. They must never be pointed at real homework projects. Fixture records and credentials are excluded via .gitignore.

## License

MIT. Contributions are welcome.