🥗 MCP Nutrition - AI Meal Planner

Generate a personalized 7-day meal plan from your profile and goal, get daily calorie catch-up when you fall short, and track your progress over time. It runs as a FastAPI web app and, because the same engine is exposed over the Model Context Protocol (MCP), also as AI tools, resources, and prompts any MCP client (Claude Desktop, etc.) can use.

The intelligence is a deliberate blend:

• 🤖 OpenAI proposes concrete, varied meals for each day.

• 🥦 API Ninjas looks up macros for each food when your key returns them. Heads-up: the free API Ninjas tier gates calories/protein, so in practice many values fall back to the bundled ~29-food catalog or an Atwater estimate (see Data & limits).

• 🌤️ OpenWeather nudges calories/hydration for the day's conditions.

• 🧮 Deterministic Python computes the targets, keeps the LLM out of the arithmetic, reconciles each day to the calorie goal, and runs the catch-up math - the parts that are guaranteed and unit-tested.

Runs offline too: with no API keys, it falls back to a bundled food catalog + cache, so git clone && run works immediately.

Quickstart

# 1. Install (uv recommended)
uv sync --extra dev

# 2. (optional) add API keys for live LLM meals + real macros
cp .env.example .env      # then edit .env

# 3. Run the web app
uv run uvicorn app.main:app --reload
# open http://127.0.0.1:8000

Prefer pip? python -m venv .venv && . .venv/Scripts/activate && pip install -e ".[dev]".

Environment (all optional)

Related MCP server: Meal Planner AI MCP

Run it as an MCP server

The planner is also a stdio MCP server named nutrition_db:

uv run nutrition-mcp        # or:  python -m mcp_server

Register it in an MCP client (e.g. Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "nutrition_db": {
      "command": "uv",
      "args": ["run", "nutrition-mcp"],
      "cwd": "/absolute/path/to/MCP Nutrition"
    }
  }
}

It exposes all three MCP primitives, not just tools:

• Tools: get_food_nutrients, get_current_conditions, compute_targets, generate_weekly_meal_plan, log_daily_intake, adjust_daily_calories.

• Resources (read-only context): nutrition://catalog, nutrition://profile, nutrition://targets, nutrition://log/today, nutrition://history.

• Prompts (guided flows): plan_my_week, log_my_meal, what_should_i_eat_now.

How it works

flowchart LR
    UI["Web UI (form)"] -->|HTTP| API["FastAPI · app/"]
    MCP["MCP client<br/>(Claude Desktop)"] -->|tools · resources · prompts| SRV["nutrition_db · mcp_server/"]
    API --> CORE
    SRV --> CORE
    subgraph CORE["core/ engine - single source of truth"]
        direction LR
        T["targets<br/>(BMR→TDEE→goal)"] --> P["planner"]
        P --> R["reconcile ±10%"]
        ADJ["catch-up adjuster"]
        H["history"]
    end
    P -->|propose foods| OA["OpenAI"]
    P -->|ground macros| AN["API Ninjas"]
    T -->|weather adjust| OW["OpenWeather"]

core/ is the single source of truth; the web app and the MCP server are thin layers over it. See docs/DECISIONS.md for the design tradeoffs and roadmap.

Plan generation (core/planner.py), per day: OpenAI proposes foods → API Ninjas returns real macros (cached) → deterministic code scales portions to hit the calorie target (±10%). A rolling "avoid recently-used items" list keeps the week varied. No OpenAI key? The bundled catalog builds the day instead.

Calorie catch-up (core/adjuster.py): compares logged intake to target. If you're under, it suggests catch-up foods for the meals you have left today, or rolls a capped portion of the shortfall into tomorrow. (This is the opposite of naively lowering the goal when you under-eat.)

Targets (core/targets.py): Mifflin-St Jeor BMR → activity TDEE → goal adjustment (-500 weight loss / +300 muscle gain) → macro split.

Project layout

core/         deterministic engine + service facade + API/LLM clients (the brains)
mcp_server/   nutrition_db MCP server (tools + resources + prompts over core/)
app/          FastAPI backend + minimal web UI (form, plan, catch-up, progress chart)
data/         food_catalog.json (fallback), nutrition_cache.json, state.json (runtime)
evals/        plan-quality eval harness + SCORECARD.md (python -m evals)
docs/         DECISIONS.md (design tradeoffs & roadmap)
tests/        pytest suite (targets, planner, adjuster, allergens, service, evals)

Testing & evals

uv run pytest -q          # 34 tests
uv run ruff check .
uv run python -m evals    # regenerate evals/SCORECARD.md

Tests run the real engine in offline mode (deterministic via the catalog) and use small fakes to exercise the LLM path without a network call.

Plan-quality evals (evals/, scorecard: evals/SCORECARD.md) score generated plans across a golden set of profiles (goals × diets × allergies) on calorie adherence, 100% allergen safety, protein adequacy, diet compliance, and variety. CI gates the non-negotiables - measuring a non-deterministic LLM system, not just unit-testing pure functions.

Data & limits

Be clear-eyed about what this does and doesn't guarantee:

• Macro source. Authoritative macros come from API Ninjas only when your key returns them. On the free tier calories/protein are premium-gated, so values fall back to the curated ~29-food catalog (data/food_catalog.json) or an Atwater (4/4/9) estimate. The bundled catalog is what makes offline mode work.

• "On target" means calories. Days are reconciled to the calorie target (±10%); protein/carb/fat are shown as guidance and a day is flagged when protein runs low, but macros aren't enforced.

• Allergens. Typed allergies are expanded to ingredient keywords (core/allergens.py) and excluded from catalog, LLM-proposed, and catch-up foods - but it's best-effort keyword matching, not a medical guarantee. Verify ingredients yourself.

• Not medical advice. Estimates only; not for pregnancy, medical conditions, or disordered eating. Single-user, local state; no accounts or sync.

Provenance

This started as an MCP nutrition benchmark server and grew into a standalone product. All code here is original work by harmehak0173; it has no dependency on the original benchmark framework.

License

MIT © 2026 harmehak0173 - see LICENSE.