Trainzilla MCP

Trainzilla MCP

Trainzilla MCP connects Claude to your Trainzilla coach account, turning your client roster, programming, and habit tracking into something you can manage conversationally. Built for fitness coaches running their practice on Trainzilla, it lets Claude pull up client profiles and metrics, build and review workout and diet plans, create and assign habits, run quick calculations (TDEE, macros, 1RM),

Category
访问服务器

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 (23 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.
  • 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

  • [x] Write tools (habits, check-ins, sessions, plans) — confirm-gated
  • [x] Resource (client profile) + prompt (weekly review)
  • [x] Wider read coverage (plans, check-ins, sessions, billing)
  • [x] Backend trainer API keys / PAT (built in tzilla-be, local — needs PR + deploy)
  • [x] 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)

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选