mcpField
T
Trainzilla Mcp

Trainzilla Mcp

Remote
github.com/TrainzillaHealth0Updated 3d ago
Repository

About Trainzilla Mcp

A local MCP server for Trainzilla coaches, connecting MCP clients to the platform's GraphQL API. It combines live client and business data, offline fitness calculators, and write operations that require explicit confirmation; the current release is a local MVP.

Key features

  1. 01Live client and coaching data
  2. 02Offline fitness calculators
  3. 03Confirmation-gated write tools
  4. 04Client profile resource
  5. 05Weekly review prompt
  6. 06stdio and localhost HTTP modes

Use cases

  1. 01Reviewing client habits and check-ins
  2. 02Calculating calories, macros, or 1RM
  3. 03Drafting plans or scheduling sessions with confirmation
  4. 04Preparing a read-only weekly client review

MCP support

The server exposes Trainzilla data and operations as MCP tools, a client-profile resource, and a weekly-review prompt. Write tools return a preview unless called with confirm: true; it supports stdio and localhost Streamable HTTP.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://api.tzilla.live/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

Details

From the project README

tzilla-mcp (MVP, local only)

A local MCP server that lets an MCP client (Claude Desktop, Claude Code, Cursor, …) act as a Trainzilla coach. It wraps the existing GraphQL API at api.tzilla.live — no direct DB access — so all auth and business rules stay enforced by the backend.

Status: Local MVP — read tools, offline calculators, and confirm-gated write tools, plus a resource + a prompt. Not deployed anywhere. Runs entirely on your machine against your own coach login.

What it can do today (94 tools, 1 resource, 1 prompt)

Read (live data):

  • whoami, list_clients, get_client_profile
  • list_client_habits, get_habit_compliance, recent_habit_activity, master_habits
  • list_workout_plans, list_diet_plans
  • list_checkins, list_sessions, list_subscriptions, billing_summary

Calculators (offline, no network):

  • calc_tdee — BMR / TDEE / recommended calories
  • calc_macros — macro split by strategy (Standard 40/30/30, Pro g/kg, Keto)
  • calc_1rm — 1-rep-max (Epley) + %1RM weight suggestions

Write (confirm-gated): every write tool returns a preview unless called with confirm: true, so nothing changes by accident:

  • create_habit, create_master_habit, assign_master_habit
  • create_checkin (with questions), schedule_session
  • create_workout_plan, create_diet_plan

Still not exposed: deletes, payment execution/refunds, messaging, permission changes — by design.

Resource: tzilla://client/{clientId}/profile — a client's profile as JSON.

Prompt: weekly_client_review — pulls profile/habits/compliance/sessions and writes a read-only weekly review.

Setup

npm install
npm run build

Create .env (see .env.example) with a coach's tokens. Easiest source — log in to the coach web app, then in the browser console:

localStorage.getItem("token")        // -> TZ_ACCESS_TOKEN
localStorage.getItem("refreshToken") // -> TZ_REFRESH_TOKEN

The server auto-refreshes the access token via refreshAccessToken when it expires.

Run

Local (stdio) — for Claude Desktop etc.:

  • Dev: npm run dev
  • Built: npm start
  • Smoke: node scripts/smoke.mjs

Remote (Streamable HTTP) — localhost only, multi-coach:

  • Dev: npm run http · Built: npm run start:http
  • Listens on http://127.0.0.1:8787/mcp (set MCP_HTTP_PORT / MCP_HTTP_HOST).
  • Smoke: node scripts/smoke-http.mjs

Auth modes

  • stdio: uses TZ_ACCESS_TOKEN (+ TZ_REFRESH_TOKEN) from env; auto-refreshes.
  • Endpoint selection:
    • TZ_API_URL wins when set.
    • Otherwise TZ_ENVIRONMENT=staging uses https://qa-be2.tzilla.live/graphql.
    • All other cases default to https://api.tzilla.live/graphql.
  • HTTP: pass-through — each request must send the coach's API key (Authorization: Bearer tz_... or x-api-key). The server never stores tokens; it forwards the caller's key to the GraphQL API, so the backend enforces scope (multi-coach safe). API keys are minted by the backend feature below.
Backend: trainer API keys (built in tzilla-be, local — not deployed yet)
  • createApiKey(name) → returns the plaintext tz_… key once + info
  • apiKeys (list, no secret) · revokeApiKey(id)
  • Auth middleware accepts tz_ keys (header x-api-key or Bearer), resolves the owning coach, and stamps lastUsedAt. Only a SHA-256 hash is stored.

Use from Claude Desktop

Add to claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "tzilla-coach": {
      "command": "node",
      "args": ["C:/New folder/tzilla-mcp/dist/index.js"],
      "env": {
        "TZ_API_URL": "https://api.tzilla.live/graphql",
        "TZ_ACCESS_TOKEN": "<paste>",
        "TZ_REFRESH_TOKEN": "<paste>"
      }
    }
  }
}

Restart Claude Desktop, then try: "Use tzilla-coach: who am I, and list my clients."

Roadmap

  • Write tools (habits, check-ins, sessions, plans) — confirm-gated
  • Resource (client profile) + prompt (weekly review)
  • Wider read coverage (plans, check-ins, sessions, billing)
  • Backend trainer API keys / PAT (built in tzilla-be, local — needs PR + deploy)
  • Remote Streamable-HTTP transport (localhost, API-key pass-through auth)
  • Deploy the backend API-key feature; host the HTTP server (TLS) for real remote use
  • Full MCP OAuth 2.1 (replace pass-through) for a public connector
  • More resources (plans / check-in history) + prompts (e.g. "draft a plan")

Layout

src/
  config.ts   # env + tiny .env loader
  client.ts   # GraphQL client: bearer auth + refresh-on-401 + role header
  calc.ts     # offline coach math (ported from HealthMath/WorkoutMath)
  index.ts    # MCP server + tool definitions (stdio)
scripts/
  smoke.mjs   # spawns the server and lists tools (handshake check)

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.