mealforge

mealforge

Enables AI-driven weekly meal planning with structured recipes, cooking steps, and grocery lists.

Category
访问服务器

README

mealforge

AI-planned weekly meals, in a real app. Plan your week conversationally with any MCP-capable AI chat (LibreChat, Claude, and friends) — the model pushes the finished plan here, where it becomes recipe cards, a step-by-step cook mode, and a grocery list you check off in the store.

Meal-planning apps limit you to their recipe catalog. mealforge has no catalog: every recipe is generated in conversation, tailored to your household, constraints, and whatever sounds good this week. The app is the structured, shoppable, cookable home for what you and the model decide together.

How it works

┌────────────┐  MCP (streamable http)  ┌────────────────────────────┐
│ your AI    │ ───────────────────────▶│ mealforge                  │
│ chat       │  push_meal_plan          │  • weekly plan view        │
│ (LibreChat,│  search_recipes          │  • recipes + cook mode     │
│  etc.)     │  list_favorites …        │  • grocery list (derived)  │
└────────────┘                          │  • favorites + history     │
                                        └────────────────────────────┘
  1. Plan in chat. "Lots of crock pot this week, salmon once, use the pork shoulder in the freezer." Iterate until it's right.
  2. The model pushes the final plan (push_meal_plan): structured recipes — ingredients with quantities, units, and store sections — plus markdown cooking steps.
  3. mealforge derives the grocery list automatically: ingredients aggregated across recipes ("2 cups" + "1 cup" → "3 cups"), grouped by store section, checkable while you shop.
  4. Favorite what you loved. The model can recall favorites and past recipes next week ("give me one of my favorites") and avoids repeating recent meals.

Self-hosting

mkdir mealforge && cd mealforge
curl -O https://raw.githubusercontent.com/loosewiredev/mealforge/main/docker-compose.yml
docker compose up -d

The web app and MCP endpoint are now on port 8090:

  • Web UI: http://<host>:8090
  • MCP endpoint: http://<host>:8090/mcp

Data lives in a single SQLite file under ./data/ — back that folder up and you've backed up everything.

Security note: mealforge has no built-in authentication. It is designed for a household on a private network — put it on your Tailscale/VPN, or behind a reverse proxy that does auth. Do not expose it to the public internet as-is.

Set APP_URL (in the compose file or a .env next to it) to the URL your household uses, e.g. APP_URL=https://mealforge.your-tailnet.ts.net — the MCP tools return it so the model can link you to the app.

Connecting an MCP client

LibreChat

librechat.yaml:

mcpServers:
  mealforge:
    type: streamable-http
    url: http://<host>:8090/mcp
    timeout: 60000

If mealforge runs on a private IP, also exempt it from LibreChat's SSRF guard:

mcpSettings:
  allowedAddresses:
    - "<host>:8090"

Then restart the LibreChat container and enable the mealforge tools for your agent/endpoint.

Other clients

Any client that speaks MCP over streamable HTTP works — point it at http://<host>:8090/mcp. No auth headers are required (see the security note above).

Give your agent the meal-planning skill

The repo ships a ready-made agent skill at skills/weekly-meal-planning/SKILL.md. It teaches an agent the whole workflow: gather history first, draft the week in conversation, publish only on explicit command via the reliable two-step flow (create_recipe per recipe, then one push_meal_plan with recipeIds), keep ingredient data grocery-list-clean, and recover from validation errors. Strongly recommended — especially with smaller models.

  • Claude Code / Agent Skills: copy the skills/weekly-meal-planning/ directory into your skills folder (e.g. ~/.claude/skills/).
  • LibreChat: paste the body of SKILL.md into your meal-planning agent's instructions (or attach it as an agent skill/file).
  • Anything else: it's plain markdown — hand it to your agent however that client takes instructions.

MCP tools

Tool Purpose
create_recipe Save one recipe (flat payload) and get back a recipeId — the reliable first step before push_meal_plan.
push_meal_plan Push a finalized week (new recipes and/or recipeId reuses). Re-pushing the same weekStart revises the week; checked-off grocery items that didn't change stay checked.
get_recent_meal_plans Recent weeks with meal titles — for repeat-avoidance.
get_meal_plan_for_week The plan for a specific week, if any.
list_favorites Recipes the household has favorited in the UI.
search_recipes Search past recipes by title, tag, or ingredient.
get_recipe Full recipe (ingredients + steps) by id.

Development

pnpm + Nx monorepo: apps/api (Hono + tRPC + Drizzle + SQLite, serves the MCP endpoint and the built web app), apps/web (React + Vite + TanStack Router + Tailwind).

pnpm install
pnpm nx run-many -t test,build      # tests + builds
pnpm --filter @mealforge/api dev    # API on :3000
pnpm --filter @mealforge/web dev    # web on :5173 (proxies /trpc to :3000)

License

MIT

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