Edamam MCP. Analyze ingredients and filter recipes by diet.
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Edamam lets your AI agent analyze food nutrition from natural language inputs, instantly giving macro breakdowns (calories, protein, carbs, etc.).
It also searches a massive recipe database, letting you filter results by any major diet type—keto, vegan, gluten-free, high-protein, and more.
Stop guessing the nutritional content; get precise data for meal planning or recipe development.
What your AI agents can do
Analyze nutrition
Analyze any food or ingredient combination described in plain text and get an instant breakdown of calories, protein, fat, carbs, and fiber.
Search edamam recipes
Search the recipe database using multiple filters for cuisine type (e.g., Mexican) or health labels (e.g., keto-friendly).
Analyze a list of ingredients described in plain text to calculate total calories, protein, fat, carbohydrates, and fiber.
Search the recipe database and limit results instantly based on required dietary restrictions (e.g., low-carb or dairy-free).
Use natural language to check the nutritional impact of an entire meal's components.
Filter recipes based on a specific geographic or culinary style, such as Italian, Japanese, or Mexican.
Ask AI about this MCP
Supported MCP Clients
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Edamam MCP Server: 2 Tools for Food Data Analysis
Analyze nutritional content from text using `analyze_nutrition`, or find specific recipes by diet and cuisine type using `search_edamam_recipes`.
019d758canalyze nutrition
Analyze any food or ingredient combination described in plain text and get an instant breakdown of calories, protein, fat, carbs, and fiber.
019d758csearch edamam recipes
Search the recipe database using multiple filters for cuisine type (e.g., Mexican) or health labels (e.g., keto-friendly).
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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- Deploy to edge with MCPFusion framework
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Make Your AI Do More
Start with Edamam, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
When you need to know what's in your food, this server handles it. It gives your AI agent advanced nutritional intelligence, letting you ditch the guesswork. The analyze_nutrition tool lets you check the full nutritional profile for any combination of ingredients described in plain text—no structured input needed.
You just type out a meal or list of components—say, "one cup brown rice and two hundred grams chicken breast"—and it instantly returns a precise breakdown. This mechanism calculates total calories, protein, fat, carbohydrates, and fiber for whatever you throw at it. You can use this capability to cross-reference ingredients, letting your agent check the nutritional impact of an entire meal's components just by describing them in natural language.
If you’re planning meals or building a recipe concept, search_edamam_recipes pulls from a massive database. It lets you narrow down options using multiple filters. You can limit results instantly based on specific dietary needs—the tool handles things like making recipes low-carb, dairy-free, gluten-free, or vegan.
When searching for inspiration, the system doesn't just give you random stuff; it helps you filter by cuisine type. You tell your agent if you want Italian, Japanese, or Mexican cooking, and the search narrows down to that style. The tool lets you combine these filters, so you can find something keto-friendly that’s also totally Hawaiian, for example.
This server takes care of everything from ingredient analysis to complex recipe filtering.
How Edamam MCP Works
- 1 Start with a query. You either give your agent a list of ingredients (e.g., '2 eggs and toast') for analysis, or you tell it the parameters for a recipe search (e.g., 'vegan, high-fiber').
- 2 Your AI client invokes the appropriate tool (
analyze_nutritionorsearch_edamam_recipes) on the Edamam MCP Server. - 3 The server returns structured data: either a precise nutritional breakdown of the ingredients or a list of recipes matching your filters.
The bottom line is, you feed it text (ingredients or filters), and it spits out usable, structured nutritional data or relevant recipe links.
Who Is Edamam MCP For?
Dietitians, food science developers, and digital recipe curators need this. They're the ones tired of manually checking USDA databases or fighting with rigid APIs. If your job involves translating 'healthy eating' into actionable data points—whether that’s macro tracking for a client or filtering out allergens from thousands of recipes—this is what you use.
Uses analyze_nutrition to quickly check the macros of custom meal plans described in natural language, ensuring clients hit their required protein or fiber targets.
Runs search_edamam_recipes with advanced filters (e.g., 'gluten-free' AND 'low-sodium') to build recipe collections for specific markets or health trends.
Uses the server to validate nutritional claims by running analyze_nutrition against sample product ingredient lists, checking for required nutrient levels.
What Changes When You Connect
- Precise Macro Tracking: Use
analyze_nutritionto get full nutritional breakdowns for meals described in natural language. You don't need structured input; just list the ingredients. - Advanced Filtering:
search_edamam_recipeshandles 40+ diet labels, letting you filter recipes by allergen, carb count, or specific health goals (like low-fat or high-fiber). - Cuisine Specificity: Filter results not just by diet, but by global cuisine type. Need an Asian recipe? Use
search_edamam_recipesto narrow it down immediately. - Efficiency Over Manual Checks: You stop wasting time cross-referencing ingredients in separate databases. Edamam gives you the data point right away via
analyze_nutrition. - Comprehensive Data Source: This single server aggregates recipe discovery and deep nutritional analysis, making your agent a single source of truth for food content.
Real-World Use Cases
Building a Personalized Meal Plan
A client needs an anti-inflammatory meal plan. Instead of manually searching 10 sites and checking every ingredient, the agent runs search_edamam_recipes filtered by 'anti-inflammatory' (or similar) and then uses analyze_nutrition on the resulting ingredients to ensure macro balance across three meals.
Validating a New Recipe Concept
A restaurant group creates a new fusion dish. They run analyze_nutrition with the full ingredient list ('salmon, lemon zest, capers, olive oil') to get an instant macro count before sending it to testing. This saves hours of manual calculation.
Curating Allergy-Safe Menus
You are building a menu for a large event with many severe allergies. You use search_edamam_recipes and stack filters: 'vegan' + 'peanut-free' + 'dairy-free'. The agent returns only safe options, eliminating weeks of manual QA.
Nutritional Fact Check for Marketing
A competitor claims their bar is high in protein. You run analyze_nutrition on the listed ingredients and compare the results against your baseline data to verify their claim instantly, proving or disproving marketing copy.
The Tradeoffs
Treating it like a simple search engine
The developer just types 'healthy dinner' and expects one perfect recipe. This wastes time because the tool needs context.
→
Don't just ask for 'dinner.' First, run search_edamam_recipes using specific filters (e.g., 'keto' AND 'Mexican'). Then, if you want to analyze a dish from that result, use analyze_nutrition.
Mixing up the tools
Trying to run search_edamam_recipes using only ingredient names ('rice and chicken') because you think it's an analysis tool. It won't work.
→
If you have ingredients, use analyze_nutrition. If you are looking for a complete recipe that matches criteria, use search_edamam_recipes.
Over-relying on the short description
Assuming that because the tool mentions 'advanced filters,' it can handle any arbitrary filter (e.g., 'good vibes only').
→
Stick to the defined parameters: use search_edamam_recipes with recognized health labels or cuisine types. The system needs specific inputs.
When It Fits, When It Doesn't
Use this MCP Server if your core problem involves translating raw food data (ingredients) into quantifiable nutritional facts, OR if you need to find recipes based on strict dietary guardrails. You must use it if the user input is complex and non-structured.
Don't use this server if: 1) The user only needs general cooking tips or historical recipe context; Edamam provides data, not narrative. 2) They require multi-step meal balancing across different nutritional axes (e.g., 'I need a dinner that is exactly 40% carbs and 35% protein'). While the tools provide the components, building that final state machine logic must happen in your agent's code.
If you are just searching for general knowledge about food groups, an external wiki or simple search API will suffice. But if you need actionable data points—like a carb count on a Mexican recipe—you use Edamam.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Edamam. 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 2 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Checking macros across multiple ingredients is tedious work.
Right now, figuring out the nutrition for an ingredient list means opening three tabs: Google Search, the USDA database, and a dietary calculator. You copy-paste '2 eggs' into one place, then 'avocado toast' into another, and you have to manually add up the calories, protein, and fat totals yourself. It’s slow, error-prone data entry.
With this MCP Server, you just give your agent a single prompt: 'What are the macros for 2 eggs and one slice of avocado toast?' The `analyze_nutrition` tool runs the whole thing in seconds and gives you one clean JSON object with all the numbers. You get immediate, structured data.
Using Edamam MCP Server: search_edamam_recipes
Before this server, finding a recipe for someone who is vegan *and* gluten-free and also wants Thai cuisine meant running multiple web searches and cross-checking results. You’d get hundreds of recipes, most of which didn't meet all three criteria.
Now, you tell your agent to run `search_edamam_recipes` with the parameters: 'vegan,' 'gluten-free,' and 'Thai.' The server handles the complex filtering logic instantly, giving you only what works. It cuts through the noise.
Common Questions About Edamam MCP
How do I use analyze_nutrition in Edamam MCP Server? +
You pass the ingredients list as a single string to analyze_nutrition. For example, '1 cup brown rice and 200g chicken breast.' The tool returns calories, protein, fat, carbs, and fiber for that combination.
Can search_edamam_recipes filter by multiple diets? +
Yes. search_edamam_recipes supports layering filters. You can combine 'vegan' with 'peanut-free,' or 'keto' with 'low-sodium.' This limits results to recipes meeting all criteria.
What kind of data does Edamam MCP Server provide? +
It provides structured nutritional data (macros) and recipe metadata. You get quantifiable facts, not just general descriptions or links.
Does analyze_nutrition work if I use unusual measurements? +
The tool is designed to parse natural language inputs. It handles common units like 'cups,' 'grams,' and standard food items accurately.
What credentials do I need to use analyze_nutrition with Edamam MCP Server? +
You must provide an app_id and app_key, which are required for secure access. These keys authenticate your agent's requests when calling the analysis tool.
What happens if I use search_edamam_recipes with conflicting filters? +
The tool handles conflicts gracefully. If no recipes match your criteria, it returns an empty list and a specific message explaining why the search failed, allowing you to adjust parameters.
Are there rate limits when I run analyze_nutrition repeatedly? +
Yes, the Edamam API enforces defined rate limits. If your agent hits those caps, it receives an HTTP error code detailing exactly how long you need to wait before retrying.
Does search_edamam_recipes support ingredients not found in food databases? +
No. The Edamam database is strictly limited to culinary data. It will not process or return results for materials outside of the recognized food domain.
How does the natural language nutrition analysis work? +
Simply type any food description — like '2 eggs and 1 slice of toast with butter' — and Edamam's NLP engine will parse the text, identify each ingredient, and return precise calorie and nutrient data. No need for structured inputs or food IDs.
Does the API offer filters for vegan or paleo diets? +
Yes, the recipe tool provides extensive dietary and health label filters including 'vegan', 'paleo', 'keto-friendly', 'gluten-free', and many more.
Are both API endpoints completely free? +
Yes, Edamam offers a Developer tier that is free of charge for both the Recipe Search API and the Nutrition Analysis API with certain monthly limits.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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