morsel

morsel

Enables logging food meals by letting AI assistants analyze food photos and write structured meal data via MCP tools to a shared store, which can then be viewed in a dashboard.

Category
访问服务器

README

Morsel

The storehouse your AI fills. Morsel is a MCP-first, camera-first food tracker. There is no chat inside the app — you log by chatting with your existing assistant (Claude, ChatGPT) and uploading a photo; the assistant's agent reads the photo, calls Morsel's MCP tools, and writes structured food data to your store. Morsel is the data store + dashboard + agent skill; the intelligence lives in the agent you already use.

Why this exists

People chat with Claude/ChatGPT every day but can't keep context or structured data. Generic calorie apps re-implement their own AI, locked inside a silo.

Morsel flips it:

  • No in-app chat. The app has no AI brain and no chat UI.
  • MCP-first. The app is a Model Context Protocol server + a data store. Your agent connects over MCP and knows the exact data structure to write.
  • Camera-first. You upload a food photo in your chat app; the agent's vision estimates macros and calls log_meal.
  • Dashboard. A native iOS app (or PWA) renders your history, totals, and goals — reading the same store the agent writes.

Architecture (one line)

Supabase (Postgres + auth + RLS + storage) ↔ thin remote MCP server ↔ your agent (Claude/ChatGPT) and ↔ native iOS dashboard. One store, two clients.

Repo layout

morsel/
├── docs/            # design docs (start here)
├── server/          # remote MCP server (Bun + Hono + MCP SDK)
├── app/             # native iOS dashboard (SwiftUI) — reads Supabase
├── db/              # Postgres migrations + seed
├── packages/schema/ # canonical types + JSON schemas for the tool contract
├── skills/          # agent skill(s) you attach to Claude / ChatGPT
└── supabase/        # project config

Docs

  • ARCHITECTURE — components, data flow, auth, backend decision
  • DATA_MODEL — tables, enums, RLS
  • MCP_TOOLS — the tool contract (input/output schemas) — what the agent writes
  • TARGETS — computed calorie/macro goal from body metrics
  • IN_CHAT_RENDER — Tier-1 snapshot rendering inside Claude/GPT
  • ROADMAP — milestones
  • CLAUDE.md — context for any agent working in this repo (AGENTS.md is a symlink to it)

Status

Design scaffold with quality guardrails in place (strict TypeScript, anti-slop ESLint + SwiftLint, CI on every PR). Working name morsel (rename freely — it's a folder + a README).

License

MIT — see LICENSE.

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