What data sources does Nutritionix_mcp use?
It uses the Nutritionix natural nutrients API to parse food items and retrieve nutritional information.
@GuptaPurujit
Basic information
MCP Server and Client for Nutritionix API v2
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
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Nutritionix_mcp is a prototype chat application that lets users log meals in natural language. It parses food items via an MCP server backed by the Nutritionix API and returns detailed calorie and macronutrient breakdowns. It uses FastAPI for backend with WebSocket and chat endpoints, FastMCP to expose Nutritionix tools, LangChain-Ollama for tool‐calling and reasoning, and Google Mesop for the frontend.
Users interact with the chat UI by typing natural language meal descriptions (e.g., “I had 1 katori dahi and 2 chapatis”). The system automatically calls Nutritionix tools to parse the food items and returns a breakdown of calories, protein, carbs, and fats. The LLM orchestrator can be swapped by modifying llm_provider.py to use different Ollama models.
alt_measuresmel.chatIt uses the Nutritionix natural nutrients API to parse food items and retrieve nutritional information.
It requires FastAPI, FastMCP, LangChain-Ollama, Google Mesop, and the Nutritionix API credentials.
The backend exposes WebSocket and chat endpoints (FastAPI) that the Mesop frontend connects to.
Yes, the LLM orchestrator is modular; you can swap Ollama models by editing `llm_provider.py`.
Yes, it uses `alt_measures` from the Nutritionix API to handle units like ounces and litres.

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