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.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.