Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@nutrition-mcp What's the nutrition and glycemic load for 2 rotis and a katori of dal?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Work in progress. The server is not implemented yet. This README is a stub that fills in as each part ships; the reasoning behind the project is in the case study.
An MCP server that gives any assistant or agent sourced nutrition, glycemic load and micronutrient data for Indian dishes and ingredients, for any amount in grams or household measures.
Demo: to be recorded once the server runs.
Assistants answer nutrition questions about Indian food from memory, with no source and no stated portion, and they often add up a meal wrongly. That matters most for people managing diabetes or a nutrient deficiency. The authoritative Indian tables can't be redistributed, and the largest open database has few Indian dishes. Details are in the case study.
Related MCP server: nutrition-mcp-server
Search for Indian dishes and ingredients by name, including spelling variants.
Nutrition for an amount in grams or household measures such as a katori or a roti, with the source and the assumed portion stated.
Meal totals, and rankings of foods by any nutrient.
Glycemic load where an openly licensed value exists, and missing, never guessed, where it doesn't.
Daily calorie and protein targets, labelled as estimates.
The full list of planned features is in docs/requirements.md.
Not measured yet. The table will come from evals/results/summary.md once the evaluations have run.
· flowchart LR
A[MCP client] -- stdio --> B[MCP server: tools and sources resource]
B --> C[Query functions]
C --> D[(In-memory registry)]
E[Loader and validation] --> D
F[Bundled and user datasets] --> EThe server reads dataset folders at startup, validates them, computes each dish's nutrition from its ingredients, and answers tool calls from memory. The longer description is in docs/architecture.md.
Layer | Choice | Why |
Language and tooling | Python 3.12, uv, ruff, mypy, pytest | One toolchain for build, lint, types and tests |
Protocol | MCP Python SDK 2.x | Typed tools over stdio for any MCP client |
Data models | Pydantic | Validation at every boundary |
Name matching | rapidfuzz | Fuzzy matching for spelling variants |
CI | GitHub Actions | Lint, tests, image build and secret scan on every push |
git clone https://github.com/Varun-Kambali/nutrition-mcp && cd nutrition-mcp
make setup
make testmake run exits with "not implemented" until the server lands. Client setup for Claude Desktop and Claude Code will be added here.
Not available yet.
Two evaluations are planned: computed dish values against independent reference tables, and a model's accuracy with and without the tools. Neither has been run.
make test and make lint. CI runs both and builds the Docker image.
The server runs on the user's machine over stdio. There is no hosted endpoint.
src/nutrition_mcp/ server code
tests/unit/ unit tests
docs/ case study, requirements, architectureDecision records will be listed here, one line each, as they are written in docs/decisions/.
Nothing runs yet. Known limits of the finished design are in the case study.
Written as the build goes.
MIT. Planned data sources and licenses: USDA FoodData Central (public domain), CoFID 2021 (Open Government Licence v3.0) and a compendium of glycemic index values for non-Western foods (CC BY 4.0). No data is bundled yet.
This server cannot be deployed
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