NutriOptimize

Why I built it

I like YAZIO for everyday food tracking. Its product catalogue is large, the app is easy to use and its photo features are practical when a meal cannot be weighed properly, for example in a restaurant.

But if I already track calories and macros, I also want to learn from the rest of the data. NutriOptimize imports my YAZIO diary into a private database and makes it available to ChatGPT through a read-only MCP connection.

That lets ChatGPT look at micronutrients, compare several days and suggest practical changes. The plugin instructions set the personal focus, such as muscle gain, micronutrient coverage or general food quality. When an entry has no useful micronutrient data, ChatGPT can provide a conservative estimate. It is clearly labelled and never presented as a measured value.

Related MCP server: mfp-mcp

How it works

flowchart LR
    Y[YAZIO] --> S[Sync worker]
    S --> D[(PostgreSQL)]
    D --> M[Read-only MCP API]
    M --> C[ChatGPT plugin]
    A[Auth0] --> M

• PostgreSQL keeps the diary history on the self-hosted server.

• A small worker imports entries, nutrients and daily targets.

• Caddy provides HTTPS; Auth0 protects the MCP endpoint.

• ChatGPT receives authorised nutrition data, never YAZIO or database passwords.

The default setup is private and single-user. Synthetic data is included, so the whole stack can be tested before connecting a YAZIO account.

Installation

You need a Linux server, a domain or subdomain, Docker Compose, an Auth0 account and access to create a private ChatGPT plugin. Ubuntu 24.04 on Hetzner Cloud is the tested example, but it is not a requirement.

1. Secure the server

Use an Ed25519 SSH key and a normal administrator with sudo. Disable root and password SSH login only after key login has been tested. Enable automatic security updates and configure the provider and host firewalls.

Do not expose ports 5432 or 8000. The self-hosting guide explains the general setup; the Hetzner guide shows one concrete example.

2. Install NutriOptimize

Point the chosen hostname to the server, install Docker Engine with the Compose plugin and clone the repository:

sudo mkdir -p /srv
sudo git clone https://github.com/viktorkoenigbauer/nutrioptimize.git /srv/nutrioptimize
sudo chown -R "$USER":"$USER" /srv/nutrioptimize
cd /srv/nutrioptimize
./scripts/setup.sh

The setup assistant creates .env, generates local secrets and optionally asks for the YAZIO credentials. It never prints secrets and refuses to overwrite an existing configuration.

Start with the included test data:

sudo docker compose up --build -d db migrate api caddy
sudo docker compose run --rm sync python -m app.sync_once
curl https://YOUR-HOST/health/ready

After the response is {"status":"ready"}, test one live import and start the regular YAZIO sync:

sudo docker compose -f compose.yaml -f compose.yazio.yaml \
  run --rm sync python -m app.sync_once
sudo docker compose -f compose.yaml -f compose.yazio.yaml up -d sync

The YAZIO adapter notes explain the required community-documented client values and current user agent.

3. Configure Auth0

Create a separate Auth0 tenant and:

1. create an API named NutriOptimize MCP;

2. use https://YOUR-HOST/mcp/ as its identifier;

3. select RS256 and add nutrition:read;

4. enable the Resource Parameter Compatibility Profile and offline access;

5. create the intended user and disable public sign-up;

6. copy the user's immutable Auth0 ID (auth0|...);

7. register https://chatgpt.com/oauth/client.json and grant it nutrition:read.

Put the public OAuth identifiers and allowed user ID in .env, switch MCP_AUTH_MODE to oauth, then rebuild the API. The OAuth guide lists the exact fields and checks.

4. Connect ChatGPT

Create a private plugin and add the MCP declaration from the OAuth guide. Connect it, complete the Auth0 login and try:

What did I eat today, and which nutrients may still be missing?

Personal goals belong in the plugin instructions. A useful starting point is:

Analyse my nutrition with a focus on muscle gain and balanced micronutrient
intake. Treat missing nutrient values as unknown. For restaurant or photo-based
entries with incomplete data, give a conservative estimate, label it clearly
and state the main assumptions.

Security and privacy

• MCP access is read-only and cannot edit the YAZIO diary.

• Credentials stay in permission-restricted Docker secret files.

• OAuth checks the token, scope and explicitly allowed user ID.

• Auth0 receives login metadata, not diary data or YAZIO credentials.

• Missing micronutrients remain unknown in the database.

• This project provides nutrition analysis, not medical advice.

See SECURITY.md, PRIVACY.md and the security model before importing personal data.

Updating the server

Changes go through a branch, pull request and GitHub Actions. After a reviewed version reaches main, update the server with:

cd /srv/nutrioptimize
./scripts/deploy.sh

The script backs up PostgreSQL, pulls the reviewed revision, rebuilds the stack and checks that the API is ready. More details are in the deployment guide.

Project notes

The stack uses Python, FastAPI, SQLAlchemy, Alembic, PostgreSQL, the Python MCP SDK, Docker Compose and Caddy. The first beta is a single-user reference implementation for technically experienced self-hosters, not a hosted service. Its files and Git history have been checked for secrets. The project is released under the MIT License.

More detail: architecture · changelog · contributing · release checklist