This MCP acts as a bridge between an AI assistant and a personal health journey, allowing users to interact with their Yazio nutrition data through natural conversation. It essentially transforms an AI like Claude or Cursor into a dedicated health companion that can see what has been eaten, track daily progress, and log missing meals without the user ever having to open the mobile app. By connecting these two worlds, it makes maintaining a diet much more intuitive and integrated into a daily digital workflow. Beyond simple logging, the server provides a robust set of features for detailed health management. It enables AI-driven analysis of nutrition trends, allowing the model to look at weekly patterns, identify macro distributions, and offer personalized insights based on historical data. Users can search through an extensive food database to find specific nutritional information, monitor weight trends, and keep tabs on water intake. Whether the goal is to analyze eating habits or simply add a forgotten snack to a diary, the AI can handle the task instantly through a simple prompt. From a technical perspective, this tool utilizes a reverse-engineered API to facilitate communication between the Model Context Protocol and Yazio’s services. It is distributed via NPM and can be integrated into MCP-compatible clients by configuring the server with environment variables for authentication. For developers, it exposes a comprehensive suite of tools—including functions like `get_user_daily_summary`, `search_products`, and `add_user_consumed_item`—which allow an LLM to programmatically fetch data or update user logs. This provides a powerful framework for building personalized AI applications that require real-time access to a user’s private nutritional profile and fitness goals.
Category: CRM, ERP & E-commerce
Tags: diet, fitness, health, nutrition, yazio
npx yazio-mcp. You can test the connection manually using the following command: bash YAZIO_USERNAME='your_email' YAZIO_PASSWORD='your_password' npx yazio-mcpjson { "mcpServers": { "yazio": { "command": "npx", "args": ["-y", "yazio-mcp"], "env": { "YAZIO_USERNAME": "your_email@emai.com", "YAZIO_PASSWORD": "your_password" } } } } Client Configuration File Locations: * Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json * Claude Code (CLI): Create a .mcp.json file in your project folder. * Cursor: ~/.cursor/mcp.jsonget_user_daily_summary | Get daily nutrition summary | date | | get_user_consumed_items | Get food entries for a date | date | | get_user_weight | Get weight data | - | | get_user_exercises | Get exercise data | date | | get_user_water_intake | Get water intake | date | | get_user_goals | Get nutrition goals | - | | get_user_settings | Get user preferences | - | | search_products | Search food database | query | | get_product | Get detailed product info | id | | add_user_consumed_item | Add food to your log | productId, amount, date, mealType | | add_user_water_intake | Add water intake entry (cumulative value in ml) | date, water_intake | | remove_user_consumed_item | Remove food from log | itemId |get_user_daily_summary and get_user_consumed_items tools over a 7-day period, Claude can perform a deep-dive analysis. It can identify nutrient deficiencies, spot inconsistent eating windows, and provide actionable coaching advice based on the user's actual data. Example: "Analyze my nutrition data from the last 7 days. Identify which days I was lowest on protein and suggest three high-protein snacks I can add to my shopping list to prevent this next week."search_products and add_user_consumed_item to update the log instantly.get_user_goals and get_user_daily_summary. The assistant can then suggest recipes or specific food portions that fit perfectly within the user's remaining nutritional "budget" for the day. Example: "How many calories and how much protein do I have left for today? Based on that, what should I eat for dinner to hit my 150g protein goal without going over my fat limit?"get_user_weight and get_user_exercises…Part of MCP Servers
You can run Yazio MCP directly through npx by specifying npx -y yazio-mcp in your client configuration. Add the server definition with your Yazio username and password environment variables to your Claude Desktop, Claude Code, or Cursor configuration file, then restart the client to load the tools.
Yazio MCP allows language models to fetch daily nutrition summaries, view consumed items, search the food database, check weight logs, and monitor water intake. It also allows adding or deleting food entries and updating water intake data programmatically through prompt commands.
No, Yazio MCP is an unofficial community tool that uses a reverse-engineered API. Yazio does not offer an official public API, so functionality relies on undocumented endpoints that could change or break at any time.
Yazio MCP works with any Model Context Protocol client supporting stdio servers and custom environment variables, including Claude Desktop, Claude Code CLI, Cursor, and the official MCP Inspector.
Yes, Yazio MCP is open source software released under the MIT License. The code and issue tracker are publicly available on GitHub.