Edamam Unveils MCP for use by AI Agents and LLM Integrations

Software developers can get API keys from Edamam and plug the Food Database directly into their AI project. They can quickly create advanced features, such as a conversational food-logging chatbot or an intelligent barcode scanner that instantly interpret

Edamam MCP is a unified, LLM-friendly interface on top of the Edamam Food Database API. Use it to search foods, fetch nutrition, and analyze food images via REST, JSON-RPC, or a local Inspector UI.

Edamam's Vision API leverages the image recognition capabilities of large language models and marries them with Edamam’s highly accurate Nutrition Analysis API. For each dish Edamam provides dish name, ingredient list as well as detailed nutrition and di

Edamam provides access to a food and grocery database with close to 900,000 basic foods, restaurant items and consumer packaged foods.

The Model Context Protocol allows for building intuitive interfaces for food logging, nutrition data retrieval, and food search.

Edamam is building the tools for the AI era, allowing agents and LLMs to easily use its data.”

— Victor Penev

NEW YORK, NY, UNITED STATES, December 18, 2025 /EINPresswire.com/ -- Edamam, a leader in nutrition data solutions for business in the health, wellness, and food sectors, announced today the launch of a Model Context Protocol (”MCP”) for its Food Database API. The MCP allows developers and companies to build intuitive interfaces integrating Large Language Models (LLMs) applications and AI Agents to leverage Edamam’s vast and accurate food data.

“We are making it easy to build AI powered nutrition data tools. With MCP, building a nutrition app is fast and minimizes the need for developer resources,” explained Victor Penev, the Edamam’s Founder and CEO.

Software developers can get API keys from Edamam and plug the Food Database directly into their AI project. They can quickly create advanced features, such as a conversational food-logging chatbot or an intelligent barcode scanner that instantly interprets ingredients and health labels, allowing them to focus on the application's design and user experience rather than complex, low-level integration tasks.

“Edamam is building the tools for the AI era, allowing agents and LLMs to easily use its data,” added Victor Penev.

The Edamam’s API is powered by a database containing nutrition facts for 1,000,000 foods and UPC codes. Usually integrating a new AI chatbot or an LLM with the API necessitates writing complex code. With the MCP, translating the user's natural language input ("What's the nutrition in one apple?") into the precise data structure required by the API is fast and easy.

Beyond the Food Database API, Edamam will build soon MCP layer for its other APIs: Recipe Search, Meal Planning, and Nutrition Analysis. Once all APIs have MCP, the company’s clients will be able to build highly personalized natural language solutions for a wide range of diet management needs, form food logging, to meal planning, and personalized meal recommendations.

To learn more about the Edamam MCP, visit https://developer.edamam.com/mcp-edamam-food or https://github.com/edamam-llc/mcp-edamam-food.

About Edamam
Edamam organizes the world’s food knowledge and provides nutrition data services and value-added solutions to health, wellness, and food businesses. Using a proprietary semantic technology platform, it delivers real-time nutrition analysis and diet recommendations via APIs. Edamam’s technology helps customers answer their clients’ perennial question: “What should I eat?”

Edamam’s partners and clients include Nestle, Amazon, Microsoft, The Food Network, The New York Times, Hearst, and Barilla. For more information, please visit www.edamam.com or developer.edamam.com.

Victor Penev
Edamam
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