MCP server exposing a USDA-accurate food database with deterministic macro math — no hallucinated nutrition numbers.
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# usda-mcp [](https://github.com/Asquarer02/usda-mcp/actions/workflows/ci.yml) [](https://pypi.org/project/usda-mcp/) [](https://pypi.org/project/usda-mcp/) [](LICENSE) An [MCP](https://modelcontextprotocol.io) server that gives Claude a **USDA-accurate food database and deterministic macro math** — so it looks nutrition numbers up instead of recalling them, and calculates portions in Python instead of doing mental arithmetic. Ask *"build me a high-protein vegan dinner at 40g protein, 30g carb, 15g fat"* and you get an answer whose numbers are exactly right, because a linear solver produced them. ``` You: high protein vegan dinner, 40g protein / 30g carb / 15g fat Claude: 147 g Beans (Dry) ....... 37.5g pro, 0g carb, 1.5g fat 5 oz Sweet Potato ...... 2.5g pro, 30g carb, 0g fat 0.96 tbsp Olive Oil .... 0g pro, 0g carb, 13.5g fat ----------------------------------------------------------- Total .................. 40.0g pro, 30.0g carb, 15.0g fat — 415 kcal ``` ## Why this exists LLMs are unreliable at two things this domain depends on: recalling specific nutrition values, and arithmetic. Ask a model for the macros in 6 oz of chicken breast and you get a plausible number that is often wrong by 15–20%. This server removes both failure modes. It contains **no AI logic at all** — no model calls, no embeddings, no semantic search. It is a database and a pile of arithmetic. The calling model does the reasoning ("what counts as light?", "what goes with salmon?") and this server supplies every number. ## Install Requires [uv](https://docs.astral.sh/uv/) (or any Python 3.10+ environment). Nothing else — no API key, no network access, no external services. It runs f…
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