Schulcloud-MCP/docs/DEPLOYMENT.md

# Deployment

## The shape of it

```
claude.ai  ──HTTPS──▶  VPS (public IP)  ──tunnel──▶  Pi 5 (home network)
                                                      └─ Caddy ──▶ schulcloud-mcp:8080
                                                                      │
                                                                      └──▶ schulcloud-thueringen.de
```

Claude's custom connectors call the endpoint from Anthropic's cloud, so it must
be publicly reachable over real TLS — a localhost tunnel or self-signed cert
will not do. The VPS provides the public address; Caddy on the Pi terminates
TLS and obtains the certificate.

The container publishes no host port. Caddy reaches it over the shared Docker
network, so the only way in from the internet is through Caddy and then through
this server's bearer check.

## First deploy

```bash
git clone <this repo> /opt/schulcloud-mcp
cd /opt/schulcloud-mcp

cp .env.example .env
# Fill in TSC_URL and TSC_JWT_COOKIE (see docs/AUTH.md), then:
openssl rand -hex 32   # → MCP_AUTH_TOKEN

docker compose up -d --build
docker compose logs -f schulcloud-mcp
```

Expect:

```
[schulcloud-mcp] listening on 0.0.0.0:8080 — instance https://… , auth enabled, keepalive every 30min
```

`auth DISABLED` there means `MCP_AUTH_TOKEN` is empty — fix it before exposing
the service. A `keepalive: token rejected (401)` line right after startup means
the Schulcloud token is dead and needs replacing (see docs/AUTH.md); the server
will run but every tool will fail.

## Joining the existing Caddy

The Pi already runs Caddy and PostgreSQL in a Compose project. This server needs
neither a database nor its own Caddy — only a network it shares with the
existing one.

Find the network Caddy is on:

```bash
docker inspect -f '{{range $k,$v := .NetworkSettings.Networks}}{{$k}}{{"\n"}}{{end}}' <caddy-container>
```

Then in `docker-compose.yml`, set that name and mark it external:

```yaml
networks:
  caddy:
    external: true
    name: <the network name you just found>
```

Append `deploy/Caddyfile.snippet` to the Pi's Caddyfile, replacing
`mcp.example.org` with the real hostname, and reload:

```bash
docker exec <caddy-container> caddy reload --config /etc/caddy/Caddyfile
```

Two settings in that snippet matter and are easy to miss:

- **`flush_interval -1`** — MCP's Streamable HTTP transport holds a
  server-sent-events channel open. Without this, Caddy buffers it and the
  connector hangs with no error.
- **`read_timeout`/`write_timeout` of 300s** — a `search` call walks every
  course and can take tens of seconds. Caddy's defaults will cut it off.

## Ports and DNS

- DNS for the hostname points at the **VPS**, not the Pi.
- The VPS forwards 80 and 443 to the Pi's Caddy. Port 80 must work too, or
  Caddy cannot complete the ACME HTTP challenge.
- Nothing else needs to be exposed.

## Verifying from outside

```bash
curl -s https://mcp.example.org/healthz
# {"status":"ok","sessions":0}

curl -s -o /dev/null -w '%{http_code}\n' -X POST https://mcp.example.org/mcp \
  -H 'content-type: application/json' -d '{}'
# 401   ← the bearer check is live
```

If `/healthz` answers but `/mcp` returns 401 with a correct token, check that
the token in `.env` matches the one in the connector exactly — no trailing
newline from a copy-paste.

## Connecting Claude

1. claude.ai → **Settings → Connectors → Add custom connector**.
2. URL: `https://mcp.example.org/mcp`
3. Under **Advanced settings**, add the bearer token as an authorization
   header. If your organisation has no header-auth field, the server also
   accepts the token as `X-Api-Key`.
4. Enable the connector in a conversation via **+ → Add connectors**.

Ask *"which courses am I in?"* as a first check — that exercises auth, the
Schulcloud token and the API in one call.

## Running it locally instead

For Claude Code or Claude Desktop on your own machine, skip all of the above and
use stdio:

```json
{
  "mcpServers": {
    "schulcloud": {
      "command": "node",
      "args": ["/path/to/schulcloud-mcp/dist/bin/stdio.js"],
      "env": {
        "TSC_URL": "https://schulcloud-thueringen.de",
        "TSC_JWT_COOKIE": "…"
      }
    }
  }
}
```

`MCP_AUTH_TOKEN` is irrelevant in stdio mode — there is no network listener.

## Updating

```bash
cd /opt/schulcloud-mcp && git pull
docker compose up -d --build
docker compose exec schulcloud-mcp node -e "1" # sanity
npm run probe                                  # re-verify the API assumptions
```

## Operational notes

- **Restart policy** is `unless-stopped`; the container comes back after a
  reboot.
- **Sessions** are in-memory and dropped after 30 minutes idle. A restart
  invalidates them; Claude re-initializes transparently.
- **Logs** are capped at 3 × 10 MB. The Authorization header is never logged.
- **The container is read-only** with `cap_drop: ALL` and
  `no-new-privileges`, running as the unprivileged `node` user. It writes
  nothing to disk — downloads are streamed through memory, capped at
  `MAX_DOWNLOAD_BYTES` (25 MiB default).
- **The Schulcloud session has a 2-hour sliding TTL**, so the server calls
  `refresh-session` every 30 minutes. Watch for
  `keepalive: session extended, 7200s` in the logs, or run
  `npm run keepalive-status` for a summary.
- **Never leave a Schulportal tab open on the token you deployed.** It shares
  the session and its auto-logout will revoke it ~2h after login. Copy the
  cookie in a private window and close it — see docs/AUTH.md.
- **Downtime longer than two hours lapses the session** and restarting does not
  recover it: a long power cut means pasting a fresh `TSC_JWT_COOKIE`.
- **Monthly chore**: refresh `TSC_JWT_COOKIE` before its 30-day hard expiry.
  `npm run probe` reports both clocks.