mealplan-mcp

mealplan-mcp

MCP server for meal planning and grocery list generation, enabling recipe storage, meal plan creation, and automated grocery lists with ignored ingredients.

Category
Visit Server

README

Mealplan MCP

A Model Context Provider (MCP) server for meal planning and grocery list generation.

Coverage

Quick Start

# Clone the repository
git clone <repository-url>
cd mealplan-mcp

# Set up environment variable for meal plan storage
export MEALPLANPATH="/path/to/meal/plans"

# Install dependencies with uv
uv venv && source .venv/bin/activate
uv pip install -e .

# Run the server
python main.py

Features

  • Store and retrieve dish recipes with ingredients and instructions
  • Create detailed meal plans for specific dates and meal types
  • Manage ignored ingredients that should be excluded from grocery lists
  • Generate grocery lists for date ranges with automatic checkboxes
  • Full MCP (Model Context Provider) compatibility for AI-driven interfaces

Available Tools

Tool Description Example Input
create_mealplan Creates a meal plan {"meal_plan": {"date": "2023-05-01", "meal_type": "dinner", "title": "Italian Night", "cook": "Chef", "dishes": [...]}}
store_dish Stores a dish recipe {"dish_data": {"name": "Pasta", "ingredients": [...]}}
list_dishes Lists all stored dishes {}
list_mealplans_by_date_range Lists meal plans within a date range {"date_range": {"start": "2023-06-01", "end": "2023-06-07"}}
export_mealplans_to_pdf Exports meal plans to a PDF file {"date_range": {"start": "2023-06-01", "end": "2023-06-07"}}
add_ignored_ingredient Adds ingredient to ignore list {"ingredient": "salt"}
get_ignored_ingredients Gets all ignored ingredients {}
generate_grocery_list Creates a grocery list markdown {"date_range": {"start": "2023-06-01", "end": "2023-06-07"}}

Claude Desktop Setup

To use this MCP server with Claude Desktop, add the following configuration to your claude_desktop_config.json file:

macOS

# Edit the config file
open ~/Library/Application\ Support/Claude/claude_desktop_config.json

Windows

# Navigate to the config file location
%APPDATA%\Claude\claude_desktop_config.json

Configuration

Add this server configuration to your claude_desktop_config.json:

{
  "mcpServers": {
    "mealplan": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/your/mealplan-mcp",
        "run",
        "main.py"
      ],
      "env": {
        "MEALPLANPATH": "/path/to/your/meal/plans"
      }
    }
  }
}

Important:

  • Replace /path/to/your/mealplan-mcp/main.py with the actual path to your main.py file
  • Replace /path/to/your/meal/plans with your desired meal plan storage directory
  • Restart Claude Desktop after making changes

Development Setup

Prerequisites

  • Python 3.12 or higher
  • uv (recommended for dependency management)

Installation

  1. Clone the repository:

    git clone <repository-url>
    cd mealplan-mcp
    
  2. Set up the environment variable for meal plan storage:

    # macOS/Linux
    export MEALPLANPATH="/path/to/meal/plans"
    
    # Windows Command Prompt
    set MEALPLANPATH=C:\path\to\meal\plans
    
    # Windows PowerShell
    $env:MEALPLANPATH = "C:\path\to\meal\plans"
    
  3. Set up the virtual environment:

    # Using uv (recommended)
    uv venv
    source .venv/bin/activate  # On macOS/Linux
    # .venv\Scripts\activate    # On Windows
    
    # Alternative: Using venv (built-in)
    python -m venv .venv
    source .venv/bin/activate  # On macOS/Linux
    # .venv\Scripts\activate    # On Windows
    
  4. Install dependencies:

    # For basic usage (recommended)
    uv pip install -e .
    
    # For development (includes testing tools)
    uv pip install -e ".[dev]"
    
    # Alternative: Using pip
    pip install -e .              # Basic usage
    pip install -e ".[dev]"       # Development
    

Running the Application

To start the MCP server:

python main.py

This starts the server using stdio transport, making it compatible with MCP client applications.

Testing

The project includes comprehensive testing with automatic test isolation:

# Run all tests
pytest

# Run with coverage
pytest --cov=mealplan_mcp

# Generate a coverage report
pytest --cov=mealplan_mcp --cov-report=html

# Run specific test categories
pytest tests/mealplan/          # Meal plan tests
pytest tests/dish/              # Dish service tests
pytest tests/renderers/         # Renderer tests

MCP Examples

Using the MCP CLI Tool

The MCP CLI tool allows you to interact with your server directly from the command line:

# In a separate terminal, run your server
python main.py

# In another terminal, start the MCP CLI
mcp dev main.py

Project Structure

mealplan-mcp/
├── main.py                  # Entry point and MCP server definition
├── mealplan_mcp/            # Main application package
│   ├── models/              # Pydantic data models
│   │   ├── dish.py          # Dish model with ingredients and instructions
│   │   ├── ingredient.py    # Ingredient model with validation
│   │   ├── ignored.py       # Ignored ingredients model
│   │   ├── meal_plan.py     # Meal plan model with date validation
│   │   ├── meal_type.py     # Meal type enumerations
│   │   └── nutrient.py      # Nutrient information model
│   ├── renderers/           # Markdown rendering modules
│   │   ├── grocery.py       # Grocery list markdown renderer
│   │   └── mealplan.py      # Meal plan markdown renderer
│   ├── services/            # Business logic services
│   │   ├── dish/            # Dish-related services (store, list)
│   │   ├── grocery/         # Grocery list generation services
│   │   ├── ignored/         # Ignored ingredients services
│   │   └── mealplan/        # Meal plan storage services
│   └── utils/               # Utility functions
│       ├── paths.py         # Path handling with test isolation
│       └── slugify.py       # String slugification utilities
├── tests/                   # Comprehensive test suite (113 tests)
└── docs/                    # Documentation

Contributing

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature-name
  3. Commit your changes: git commit -am 'Add some feature'
  4. Push to the branch: git push origin feature-name
  5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Recommended Servers

playwright-mcp

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.

Official
Featured
TypeScript
Magic Component Platform (MCP)

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.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

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.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

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.

Official
Featured
TypeScript
Kagi MCP Server

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.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

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.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured