# HowToCook-mcp: Derivatives and Integrations of the HowToCook Repository

> Explore HowToCook-mcp, an AI ecosystem extending the HowToCook recipe database into personal chef assistants with JavaScript and Python integrations.

- Repository: [Anduin Xue/HowToCook](https://github.com/Anduin2017/HowToCook)
- Tags: overview
- Published: 2026-02-27

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**HowToCook-mcp is an ecosystem of AI-driven extensions that transform the Anduin2017/HowToCook recipe database into interactive personal chef assistants, available in both JavaScript and Python implementations.**

The open-source cookbook Anduin2017/HowToCook maintains a structured collection of markdown-based recipes. While the core repository focuses on static documentation, its **derivatives and integrations**—specifically the HowToCook-mcp ecosystem—extend this content into dynamic, conversational interfaces. These tools parse the repository's markdown files to provide intelligent meal planning capabilities.

## What Is HowToCook-mcp?

HowToCook-mcp represents the official community-maintained forks that implement the "Model Context Protocol" (MCP) pattern to expose HowToCook content through AI assistants. The project recognizes two primary implementations that consume the core repository's recipe data.

### HowToCook-mcp (JavaScript/Node)

The original **HowToCook-mcp** is a Node.js-based implementation that exposes the recipe database through a JavaScript API. According to the repository's [`README.md`](https://github.com/Anduin2017/HowToCook/blob/main/README.md) at line 455, this derivative "turns an AI assistant into a personal chef, helping you plan breakfast, lunch and dinner for the whole day."

This implementation parses markdown content from the `dishes/`, `tips/`, and `starsystem/` directories to extract structured data including ingredient lists, cooking steps, and nutritional notes.

### HowToCook-py-mcp (Python)

**HowToCook-py-mcp** provides a Python-native implementation of the same AI-assistant concept. Listed at line 456 in [`README.md`](https://github.com/Anduin2017/HowToCook/blob/main/README.md), this derivative offers identical day-meal planning capabilities through a Python-friendly API.

The Python implementation wraps the markdown parsing logic in a distributable package, making it easy to embed HowToCook content into custom scripts, data pipelines, or larger Python applications.

## How HowToCook-mcp Integrates with the Core Repository

The HowToCook-mcp ecosystem does not duplicate recipe content. Instead, it implements a read-only integration pattern that consumes the original repository's structured markdown.

### Content Source Directories

The derivatives specifically target three content directories within Anduin2017/HowToCook:

- **`dishes/`** – Contains individual recipe markdown files organized by category
- **`tips/`** – Stores cooking techniques and general kitchen advice
- **`starsystem/`** – Holds advanced or rated recipes with structured metadata

### Documentation and Template Propagation

The existence of these derivatives is documented in the project's central [`README.md`](https://github.com/Anduin2017/HowToCook/blob/main/README.md) file, ensuring visibility to all repository visitors. Furthermore, the integration is reinforced through the [`.github/templates/readme_template.md`](https://github.com/Anduin2017/HowToCook/blob/main/.github/templates/readme_template.md) file, which automatically includes references to HowToCook-mcp and HowToCook-py-mcp in any new forks of the repository.

This template-based approach ensures that the ecosystem of derivatives remains discoverable regardless of how many community forks exist.

## Using HowToCook-mcp: Code Examples

Both implementations expose a similar API pattern centered on the `planDay` (JavaScript) or `plan_day` (Python) method, which generates full-day meal recommendations based on dietary constraints and caloric targets.

### JavaScript/Node Implementation

```javascript
// Import the AI-assistant client provided by the HowToCook-mcp repo
const CookAssistant = require('howtocook-mcp');

// Initialise with an optional OpenAI key (handled internally)
const assistant = new CookAssistant();

// Ask for a full-day menu
assistant.planDay({
  dietary: 'vegetarian',
  calories: 1800,
}).then(menu => console.log('Your personalized menu:\n', menu));

```

### Python Implementation

```python
from howtocook_py_mcp import CookAssistant

assistant = CookAssistant()

# Generate a three-meal plan for a low-carb diet

menu = assistant.plan_day(diet='low-carb', target_kcal=1500)

print("Today's menu:")
for meal, recipe in menu.items():
    print(f"{meal.title()}: {recipe['title']} – {recipe['link']}")

```

Both examples demonstrate the core integration pattern: initialization loads the parsed markdown data from the source directories, the planning method applies AI-driven selection logic, and the returned objects contain direct references to the original recipe files in the `dishes/` directory.

## Summary

- **HowToCook-mcp** is the official ecosystem of AI-driven derivatives extending the Anduin2017/HowToCook repository.
- Two primary implementations exist: **HowToCook-mcp** (JavaScript/Node) and **HowToCook-py-mcp** (Python).
- Both integrations consume markdown content from the `dishes/`, `tips/`, and `starsystem/` directories without duplicating data.
- The derivatives are documented in the main [`README.md`](https://github.com/Anduin2017/HowToCook/blob/main/README.md) (lines 455-456) and propagated through [`.github/templates/readme_template.md`](https://github.com/Anduin2017/HowToCook/blob/main/.github/templates/readme_template.md).
- Core functionality centers on the `planDay`/`plan_day` methods for generating personalized meal plans based on dietary constraints.

## Frequently Asked Questions

### What is HowToCook-mcp and how does it relate to the main repository?

HowToCook-mcp is a community-maintained ecosystem of AI assistants that transform the static recipe collection in Anduin2017/HowToCook into interactive meal planning tools. Rather than forking the content, these derivatives integrate directly with the original repository's markdown files in the `dishes/` and `tips/` directories, treating the main repo as a read-only data source.

### What are the differences between HowToCook-mcp and HowToCook-py-mcp?

**HowToCook-mcp** is the original JavaScript/Node.js implementation, while **HowToCook-py-mcp** provides a Python-native alternative. Both offer identical core functionality—specifically the `planDay` (JS) and `plan_day` (Python) methods for generating daily meal plans—but target different runtime environments. The Python version is packaged for easy integration into data science workflows, whereas the Node version fits JavaScript application stacks.

### How do these derivatives access the recipe data without duplicating it?

The integrations use a read-only consumption pattern. They parse the markdown files located in `dishes/`, `tips/`, and `starsystem/` directly from the source repository. This approach ensures that any updates to recipes in the main Anduin2017/HowToCook repo are immediately reflected in the AI assistants without requiring manual synchronization or content duplication.

### Where are these integrations documented in the source code?

The derivatives are officially listed in the main [`README.md`](https://github.com/Anduin2017/HowToCook/blob/main/README.md) file at lines 455 and 456, which describe HowToCook-mcp and HowToCook-py-mcp respectively. Additionally, these references are hardcoded into the [`.github/templates/readme_template.md`](https://github.com/Anduin2017/HowToCook/blob/main/.github/templates/readme_template.md) file, ensuring that any new forks of the repository automatically inherit documentation pointing to these ecosystem projects.