Recipe Manager MCP Server
Enables managing recipes via a web UI and MCP tools, allowing retrieval and saving of recipe data through natural language.
README
Recipe Manager MCP Server
A FastMCP server built with FastAPI that serves a recipe editing web page and exposes recipe data via MCP tools. Designed for voice-assistant interaction — fractions like 1/2 and 1¼ are automatically normalized to "a half" and "one and a quarter" so they read naturally aloud.
Features
- Web UI — Clean, responsive form with Edit and Import tabs at
/ - Recipe Import — Paste plain-text recipe blocks and auto-parse into structured fields
- Fraction Normalization —
1/2,1¼,½→"a half","one and a quarter","a half"on save - Notes Field — Optional notes section for tips, substitutions, and comments
- REST API —
GET /api/get-recipe,POST /api/save-recipe,POST /api/import-recipe - MCP Tools —
get_recipe,save_recipe, andimport_recipeexposed at/mcp/ - Persistent storage — Recipes saved to
recipes.jsonin a Docker named volume
Quick Start
Running with Docker (recommended)
bash run.sh
This rebuilds the image and starts the container on port 8002 with a named volume (recipe-data) for persistent storage and --restart unless-stopped for auto-recovery.
Running locally
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn server:app --host 0.0.0.0 --port 8000
Web UI
Open http://localhost:8002 in your browser.
Edit Tab
Four fields: Recipe Name, Ingredients, Directions, and Notes. Edit any field and click Save Recipe to persist changes. Fractions are normalized on save so voice assistants read them naturally.

Import Tab
Paste a plain-text recipe block and click Parse & Save. The parser extracts the title, ingredients, directions, and notes, then switches to the Edit tab so you can review before saving.

Expected format:
Recipe Name Here
Ingredients:
1 1/2 cups flour
1/4 tsp salt
2 eggs
Directions:
Mix ingredients together.
Bake at 350°F for 25 minutes.
Notes:
Add vanilla extract for extra flavor.
The first line becomes the recipe title. Sections are identified by Ingredients:, Directions:, and Notes: headers (case-insensitive). Nutrition facts and source URLs are ignored.
Endpoints
| Endpoint | Method | Description |
|---|---|---|
/ |
GET | Web UI for editing and importing recipes |
/api/get-recipe |
GET | Get the current recipe as JSON |
/api/save-recipe |
POST | Save a recipe (JSON body with name, ingredients, directions, notes) |
/api/import-recipe |
POST | Import a plain-text recipe (JSON body with text) |
/mcp/ |
POST | MCP Streamable HTTP transport endpoint |
MCP Tools
get_recipe— Retrieve the current recipesave_recipe— Save or update a recipe (params:name,ingredients,directions,notes)import_recipe— Parse and save a plain-text recipe block (param:text)
Import via MCP example
import_recipe(text="Classic Pancakes\n\nIngredients:\n1 1/2 cups flour\n1/4 tsp salt\n\nDirections:\nMix and cook on a griddle.\n\nNotes:\nServe with maple syrup.")
Fraction Normalization
On save, fractions in ingredients, directions, and notes are converted to voice-friendly words:
| Input | Output |
|---|---|
1/2 cup |
a half cup |
1 1/2 cups |
one and a half cups |
3/4 tsp |
three quarters tsp |
1¼ cups |
one and a quarter cups |
½ tsp |
a half tsp |
80/20 ground beef |
80/20 ground beef (unchanged) |
This ensures voice assistants read measurements naturally instead of saying "one slash two."
Project Structure
recipe-mcp/
├── Dockerfile # Container build definition
├── .dockerignore # Docker build context exclusions
├── .gitignore # Git exclusion rules
├── README.md # This file
├── data.py # Recipe I/O, Pydantic model, and import helper
├── mcp_server.py # FastMCP tools definition
├── parser.py # Plain-text recipe parser and fraction normalizer
├── requirements.txt # Python dependencies
├── run.sh # One-command rebuild + restart (port 8002)
├── sample_1.txt # Sample recipe for testing import
├── sample_2.txt # Sample recipe for testing import
├── server.py # FastAPI app, routes, and entry point
└── templates/
└── web_page.html # Web UI template with Edit/Import tabs
Recipe data is persisted in a Docker named volume (recipe-data) and is not tracked by git.
推荐服务器
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。
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。
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。
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。
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。
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。
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。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
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。