Skip to main content
Glama
jirathip-dev

morsel

by jirathip-dev

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.

Related MCP server: MyFitnessPal MCP Server

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.

A
license - permissive license
Not graded
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • -
    license
    Not graded
    quality
    Not graded
    maintenance
    A calorie tracking MCP server that processes food photos and text descriptions to automatically log nutrition data to Google Sheets. Integrates with Apple Health for fitness metrics and provides SMS interface for convenient meal logging.
  • A
    license
    Not graded
    quality
    A
    maintenance
    Connect MyFitnessPal to Claude or any MCP client. Log meals, search food database with macros, track trends, and export nutrition history against your real MyFitnessPal diary.
    6
    MIT

View all related MCP servers

Related MCP Connectors

  • Connect e-commerce and marketing data to AI assistants via MCP.

  • OCR, transcription, file extraction, and image generation for AI agents via MCP.

  • Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jirathip-dev/morsel'

If you have feedback or need assistance with the MCP directory API, please join our Discord server