io.github.topness-msft/salonrunner-mcp

salonrunner-mcp

An MCP server that lets an AI assistant find, book, and cancel salon appointments through your personal SalonRunner / Rosy Salon Software client account — the same booking site many salons use.

It's self-hosted: you deploy your own instance. Run it locally as a tool (Claude Desktop, Cursor, Copilot CLI) where credentials stay on your machine, or as a remote connector for claude.ai where you log in with your SalonRunner account on the connector's login screen.

⚠️ Unofficial, uses undocumented endpoints, personal use only. Read DISCLAIMER.md.

Tools

How it works

list/find/book/cancel
        │
   this server ──login──► app.salonrunner.com  (session cookie)
        │      ──authv2─► customer JWT (30 min, auto-refreshed)
        │      ──reads──► app.rosysalonsoftware.com/api/v2  (Bearer JWT)
        └──────writes───► /customer/appointments/{book,cancel}.json  (cookie)

customerId is discovered from your account after login; corporateId is read from the JWT. Availability is computed from the provider's per-service duration and the salon's slot grid (SALONRUNNER_SLOT_MINUTES, default 15).

Configuration

There are two ways to run it, and they get their salon credentials differently:

• Local (stdio): credentials come from the environment (.env).
• Remote (HTTP, claude.ai): credentials are entered on the connector's login screen and encrypted into the token — the server needs no salon credentials in its environment.

In HTTP mode the salon id + username + password are collected on the login screen (validated by a real SalonRunner login) and encrypted into the OAuth token, so one deployment can serve multiple salons and there are no salon secrets on the server.

Option A — Local (Claude Desktop / Cursor / Copilot CLI)

Published on npm as salonrunner-mcp. Install globally:

npm install -g salonrunner-mcp

Then point your client at the salonrunner-mcp command (no build, no absolute paths). Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "salonrunner": {
      "command": "salonrunner-mcp",
      "env": {
        "SALONRUNNER_SALON_ID": "21248",
        "SALONRUNNER_USERNAME": "you@example.com",
        "SALONRUNNER_PASSWORD": "your-password"
      }
    }
  }
}

Prefer no global install? Use "command": "npx" with "args": ["-y", "salonrunner-mcp"] and the same env.

Run from a clone instead

npm install && npm run build

Then use "command": "node" with "args": ["/absolute/path/to/salonrunner-mcp/dist/stdio.js"] and the same env.

No hosting, no OAuth — credentials stay on your machine. Recommended if you don't need claude.ai.

Option B — Remote (claude.ai custom connector)

claude.ai can only use remote MCP servers, so you deploy your own instance.

Deploy to Fly.io

fly launch --no-deploy          # pick a unique app name; creates the app
fly secrets set \
  SESSION_SIGNING_KEY=$(node -e "console.log(require('crypto').randomBytes(32).toString('base64url'))") \
  PUBLIC_URL=https://YOUR-APP.fly.dev
fly deploy
fly scale count 1               # in-memory MCP sessions: keep a single instance

No salon credentials are configured here — users supply them on the login screen. The server refuses to start without SESSION_SIGNING_KEY. Credentials are validated by a real SalonRunner login and then encrypted into the (signed) token, which claude.ai stores, so the app scales to zero between uses and you authorize only once — cold starts (~3s) are transparent and never re-prompt.

Connect in claude.ai

1. Settings → Connectors → Add custom connector.
2. URL: https://YOUR-APP.fly.dev/mcp
3. Claude opens the connector's login screen → enter your salon id + username + password.
4. The six tools appear in chat.

Run the remote server locally (testing)

SESSION_SIGNING_KEY=local-dev-please-change PUBLIC_URL=http://localhost:8787 npm run start:http

Security model

Two independent auth layers:

1. claude.ai ↔ this server — OAuth 2.1 (PKCE + dynamic client registration). The login screen authenticates the user with a real SalonRunner login; the credentials are then AES-GCM encrypted and embedded inside the HMAC-signed token (keyed by SESSION_SIGNING_KEY). No server-side session store, so authorization survives restarts and scale-to-zero.
2. this server ↔ SalonRunner — login → session cookie → short-lived JWT, auto-refreshed, using the credentials decrypted from the caller's token.

The server holds no salon credentials at rest — they live (encrypted) inside each user's token and are only decrypted in memory per request. One deployment can serve multiple salons. A leaked token can't be revoked individually; rotate SESSION_SIGNING_KEY to invalidate all tokens (everyone re-enters credentials once). Keep SESSION_SIGNING_KEY secret and serve only over HTTPS.

Notes & limitations

• Built on undocumented customer endpoints; they can change without notice. Base URLs are configurable so you can adapt quickly.
• Real bookings/cancellations incur the salon's cancellation-policy fees. Tools surface the service/provider/time before acting; consider running with SALONRUNNER_READ_ONLY=true first.
• SALONRUNNER_SLOT_MINUTES must match your salon's scheduling grid (default 15) for accurate availability.
• The officially documented, partner-only Rosy Salon Software API (api.salonrunner.com) is a separate product requiring a salon-issued ApiKey; this project does not use it.

License

MIT — see LICENSE. No warranty.