FatSecret Platform MCP. Audit nutrient profiles and food records via AI.
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FatSecret Platform connects your AI agent to a massive food and nutrition database. Use it to audit recipes, search for thousands of food items, and retrieve detailed nutrient metadata—like calories, fats, and proteins—without touching a diet app.
Your agent acts like a real-time dietitian, handling everything from barcode lookups (`get_food_by_barcode`) to full recipe analysis (`get_recipe_details`).
What your AI agents can do
Get food by barcode
Looks up food item details using a UPC or EAN barcode number.
Get food details
Pulls the full nutrient profile for a specific food ID.
Get recipe details
Retrieves all data, including prep steps and nutrients, for a specific recipe ID.
You provide a UPC or EAN barcode, and the agent returns the associated food item details.
You give a specific food ID, and the agent pulls a full breakdown of its nutrient composition.
You input a recipe ID, and the agent fetches the full recipe, including preparation steps and nutritional totals.
You ask for a food item (e.g., 'avocado'), and the agent searches the database for matching entries.
You provide keywords (e.g., 'salmon'), and the agent finds relevant recipes in the database.
The agent retrieves a list of high-level food categories, helping you understand the data structure.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
019d843aget food by barcode
Looks up food item details using a UPC or EAN barcode number.
019d843aget food details
Pulls the full nutrient profile for a specific food ID.
019d843aget recipe details
Retrieves all data, including prep steps and nutrients, for a specific recipe ID.
019d843alist food categories
Returns a list of high-level food categories available in the database.
019d843asearch foods
Searches the database for food items based on keywords or partial names.
019d843asearch recipes
Finds recipes that match keywords or ingredients provided.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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What you can do with this MCP connector
FatSecret Platform connects your AI agent to a massive food and nutrition database. You'll use it to audit recipes, search for thousands of food items, and pull detailed nutrient metadata—like calories, fats, and proteins—without needing to open a diet app. Your agent acts like a real-time dietitian, handling everything from barcode lookups to full recipe analysis.
You can search for food items using keywords, which lets you find things like 'avocado.' If you've got a UPC or EAN barcode, your agent can use get_food_by_barcode to look up the specific food item details. You can also tell it to search for recipes using keywords, and it'll find relevant recipes using search_recipes.
If you know a specific food ID, you can get a full nutrient profile using get_food_details. You can pull all the data—including prep steps and nutritional totals—for a whole recipe by giving it a recipe ID with get_recipe_details. You can check what kinds of food are in the database by calling list_food_categories.
The platform lets you pull the full nutrient profile for a food item using get_food_details, which is key for accurate meal planning.
How FatSecret Platform MCP Works
- 1 First, you subscribe to the server and provide your FatSecret Client ID and Client Secret.
- 2 Next, you connect the server to your AI client (like Cursor or Claude).
- 3 Finally, you tell your agent what you need—for example, 'What are the nutrients in a cookie using barcode 012345?'—and it executes the necessary tool calls.
The bottom line is, your AI agent handles the connection and the complex API calls, letting you talk to the data instead of writing code.
Who Is FatSecret Platform MCP For?
Nutritionists, health-tech developers, and research leads use this. If you deal with food data, recipes, or nutrient tracking, this is for you. You need a reliable source that structures raw food data so your agent can use it in conversation, eliminating the need for manual database lookups or messy web scraping.
Checks client food logs by querying specific food IDs to verify nutrient content and plan balanced meals.
Builds nutritional trackers and validation services by using the get_food_by_barcode tool to ensure product data accuracy.
Audits recipe databases using get_recipe_details to extract preparation metadata and calculate precise nutritional breakdowns.
What Changes When You Connect
- Barcode Accuracy: Use
get_food_by_barcodeto query branded products. You eliminate guesswork and ensure the nutrient data matches the actual packaged item. - Deep Recipe Analysis:
get_recipe_detailsgives you more than just ingredients. You pull preparation steps and the full nutrient breakdown for an entire recipe. - Structured Research: Instead of sifting through messy websites,
search_foodsandsearch_recipeslet your agent find structured data using natural language, instantly. - Targeted Data Pull:
get_food_detailsrequires a specific food ID. This ensures you get precise, verifiable nutrient data for analytical meal planning, not general estimates. - Data Governance:
list_food_categoriesshows you the top-level structure of the data. You understand the data's boundaries and organization immediately. - Workflow Control: The combination of these tools allows you to build multi-step processes. For instance, you can
list_food_categoriesfirst, thensearch_foodswithin a category, and finallyget_food_details.
Real-World Use Cases
A client reports unusual nutrient intake.
A dietitian receives a client's food log. Instead of manually looking up every item, the agent uses get_food_by_barcode for verification and then calls get_food_details on the resulting IDs. This immediately flags any nutrient discrepancies, allowing the dietitian to adjust the plan in real-time.
A developer needs to validate a new food data entry.
A health-tech developer needs to confirm if a recipe is balanced. They use search_recipes with keywords like 'breakfast' and 'high protein'. They then select a candidate recipe and run get_recipe_details to pull the full nutritional and preparation metadata, confirming the recipe meets internal standards.
A researcher needs to map global food data structures.
A culinary researcher wants to understand the full scope of available data. They first use list_food_categories to see the hierarchy. Then, they use search_foods to test data retrieval within a specific category, building a complete map of the dataset's structure.
A team needs to automate ingredient auditing for a product launch.
The operations lead wants to audit all recipes for a new product line. They use search_recipes to find all relevant recipes, pass the IDs to get_recipe_details to pull the nutritional data, and finally use get_food_details on the key ingredients to verify macro-nutrient compliance across the board.
The Tradeoffs
Guessing the right tool
Trying to get nutrient data just by searching for a name. The user might run search_foods('apple') and get a list of results, but they don't know which ID to use for the final nutrient breakdown.
→
First, use search_foods to find the desired item. Once you have the specific food ID, call get_food_details with that exact ID. This sequence guarantees you pull the precise, verifiable nutrient profile you need.
Treating data as a single search result
Assuming a recipe's nutrients are always visible in the search results. The user runs search_recipes('chicken') and only sees the title and keywords, not the actual caloric count or full preparation steps.
→
To get the full picture, you must run search_recipes first to find the recipe ID. Then, pass that ID to get_recipe_details. This action forces the agent to retrieve the complete, structured data payload.
Ignoring the barcode data source
Using a general search when the user knows the exact packaged product code (UPC/EAN). This wastes time searching when the definitive answer is available.
→
Always start with get_food_by_barcode. If you have the barcode, this is the fastest and most reliable way to get accurate, branded product data. It bypasses general keyword searches entirely.
When It Fits, When It Doesn't
Use this server if your core task is auditing, verifying, or analyzing structured food and nutrition data. You need to get verifiable nutrient profiles, pull recipe metadata, or look up specific UPC codes. Don't use this if you just want general, unstructured information (e.g., 'What's a good breakfast?'). For that, you need a conversational AI model that doesn't rely on external APIs. If you need to process the data (e.g., comparing nutritional profiles across 50 items), you'll need to chain multiple tools like get_food_details and search_foods within a single agent workflow.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FatSecret Platform. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually checking nutrition facts across multiple sources is a massive time sink.
Today, checking a client's nutrient intake means opening the food app, searching for the item, finding the right serving size, and then manually cross-referencing calories, fat, and protein. If you're auditing a recipe, you're copy-pasting ingredients and nutritional data into a spreadsheet, hoping nothing gets missed. It's slow, and it's prone to human error.
With this MCP server, your agent handles the whole process. You ask it to 'Audit this meal'—it runs `get_food_details` for every component and pulls the full breakdown. You get a single, accurate JSON output that eliminates manual data entry and cross-referencing.
FatSecret Platform MCP Server: Get full recipes and food data instantly.
Before this, finding a recipe meant searching general websites, then manually picking out the ingredient list, and finally trying to find the nutritional breakdown elsewhere. The steps were fragmented and unreliable.
Now, your agent uses `get_recipe_details`. It pulls the full preparation metadata, the ingredient list, *and* the total nutritional count in one go. The data is structured, and you can act on it immediately.
Common Questions About FatSecret Platform MCP
How do I use the `get_food_by_barcode` tool? +
The get_food_by_barcode tool requires a UPC or EAN number as input. It returns the specific food item and its detailed nutrient metadata, allowing you to verify branded product data quickly.
What is the difference between `search_foods` and `get_food_details`? +
search_foods finds items by name, giving you a list of options and IDs. get_food_details requires a specific ID and returns the complete, detailed nutrient breakdown for only that one item.
Can I use `search_recipes` for general meal ideas? +
While search_recipes finds recipes by keywords, it's not for general ideas. It requires you to select a specific recipe ID before you can run get_recipe_details to get the full nutritional and preparation data.
Do I need to call `list_food_categories` first? +
No, you don't need to call list_food_categories first. Use it when you need to understand the organizational structure of the dataset, but it's not required to run the core search functions.
What is the best way to audit a product using `get_food_by_barcode`? +
Use get_food_by_barcode with the UPC/EAN. This is the most reliable method for verifying packaged goods, as it pulls data directly from the source.
How do I use `get_food_details` for meal planning? +
You pass a specific food ID to get_food_details. This returns comprehensive nutrient data, including calories, fats, and proteins. You use this data to build out analytical meal plans or check client intake.
What is the difference between `search_foods` and `get_food_details`? +
Wait, I already covered this. I need to make sure I pick a different topic.
How do I find my FatSecret credentials? +
Log in to the FatSecret Platform API portal, register a new Application, and you will find your Client ID and Client Secret under the 'API Key' section. Copy and paste them below.
Can the agent search for recipes? +
Yes. Use the search_recipes tool. Your agent will retrieve matching recipes from the FatSecret database, including descriptions and nutritional highlights.
Is barcode lookup supported? +
Yes. The get_food_by_barcode tool allows your agent to identify a food ID in the FatSecret database using a standard UPC or EAN barcode.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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