flowya-mcp

flowya-mcp

Hosted MCP server that exposes Flowya tasks, spaces, and weekly goals to AI clients like Cursor, Claude, and Cowork with per-user authentication and isolation.

Category
访问服务器

README

flowya-mcp

Hosted, multi-tenant MCP server that exposes each Flowya user's tasks, spaces, and weekly goals to AI clients (Cursor, Claude, Cowork).

It is a thin tool layer over the same Supabase database Flowya's macOS/iOS apps use. Every request is scoped to the calling user, so it is safe to run one shared endpoint for all users.

How it works

AI client (Cursor / Claude / Cowork)
   │  HTTPS POST /mcp  +  Authorization: Bearer fmcp_...
   ▼
flowya-mcp (Vercel)  ──service role + per-user ownership checks──▶  Supabase
  • Transport: Streamable HTTP at POST /mcp (stateless: a fresh MCP server per request). A local stdio entrypoint exists for development.
  • Auth (v1): Personal Access Tokens (PAT, prefix fmcp_). A user generates one from Flowya ("Connect with AI"); the client sends it as a Bearer token. See src/auth.ts.
  • Isolation: the server uses the Supabase service role but scopes every query by the verified user_id and checks resource ownership (spaces/todos/goals) before any read or write. See src/api/.

Tools

Tool Description
list_spaces List the user's spaces (fronts)
get_tasks List tasks with filters (space, status, priority, archived, due dates)
create_task Create a task in a space
update_task Partial update (status changes set started_at/completed_at)
reorder_tasks Set task order/positions
archive_task Soft-delete a task
get_weekly_goals Current week's goals (optionally last week too)
set_weekly_goals Replace the current week's goals
link_goal_to_tasks Attach tasks to a weekly goal

The server also declares instructions so agents treat Flowya as the single source of truth for tasks and prioritize by impact on goals.

Setup

  1. Install: npm install
  2. Create .env from .env.example:
    • FLOWYA_SUPABASE_URL, FLOWYA_SUPABASE_ANON_KEY — same values Flowya uses (VITE_SUPABASE_URL / VITE_SUPABASE_ANON_KEY).
    • SUPABASE_SERVICE_ROLE — the project's service role key (server only).
  3. Apply the token table migration to the Supabase project: Flowya-MacOS/supabase/migrations/create_mcp_tokens_table.sql
  4. Typecheck/build: npm run typecheck / npm run build

Run locally

  • HTTP server: npm run start (defaults to :8787). Health: GET /health.
  • Stdio (dev, no token — acts as one user): set FLOWYA_DEV_USER_ID and run npm run mcp.

Deploy (Vercel)

The repo is Vercel-ready:

  • vercel.json builds dist/ via tsc and routes all traffic to api/index.js, which calls the shared dispatch from dist/http.js.
  • Set env vars in the Vercel project: FLOWYA_SUPABASE_URL, FLOWYA_SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE, PUBLIC_API_URL (the deployed URL).
  • Deployment protection (Vercel Authentication / SSO) must be OFF so the /mcp endpoint is publicly reachable.
  • Optional: point a custom domain (e.g. mcp.flowya.app) at the deployment.

Currently deployed at https://flowya-mcp.vercel.app (MCP endpoint: https://flowya-mcp.vercel.app/mcp).

vercel --prod   # from this directory, after `vercel link`

How a user connects

  1. In Flowya, open Settings → Connect with AI and generate a token (fmcp_...). It is shown once.
  2. Add the remote MCP server to their client with the URL https://flowya-mcp.vercel.app/mcp and header Authorization: Bearer fmcp_....

Example Cursor ~/.cursor/mcp.json:

{
  "mcpServers": {
    "flowya": {
      "url": "https://flowya-mcp.vercel.app/mcp",
      "headers": { "Authorization": "Bearer fmcp_YOUR_TOKEN" }
    }
  }
}

The Flowya desktop app talks to this server's /v1/tokens endpoints (authenticated with the user's Supabase session) to create, list, and revoke tokens. Set VITE_FLOWYA_MCP_URL in Flowya-MacOS if the MCP is not at the default https://flowya-mcp.vercel.app.

Roadmap (v2)

OAuth 2.1 (dynamic client registration + PKCE) for a one-click "Connect" experience, replacing manual token paste. The transport, tools, and api/ layer are reused unchanged; only an OAuth token-issuance layer and a consent page are added.

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
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
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
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