io.github.davidmosiah/wellness-nourish

Review the day after a meal log and explain what changed plus the next correction or action.

Return agent-facing install, safety, resource, and first-call guidance.

Route agent-provided food image observations across barcode, nutrition label OCR, or meal-photo estimation without logging.

Log multiple intake entries in a single call. Each item is processed through the same text-estimator pipeline as `nourish_log_intake`, but the entire batch shares one explicit_user_intent flag — perfect for Telegram users who say 'log everything I ate today: breakfast was X, lunch was Y, dinner was Z'. Returns per-item success/failure so a partial failure doesn't lose the rest.

Describe supported nutrition workflows, providers, and recommended first tools.

Estimate the carbon footprint (kg CO2-equivalent) of a meal, plus optional lower-carbon swap suggestions. Pass `items: [{name, grams}, ...]` for an arbitrary meal, OR `date: YYYY-MM-DD` to compute carbon over that day's logged intake. Data: Agribalyse 3.1 (Etalab Open License) + Our World in Data / Poore & Nemecek 2018 (CC-BY 4.0). Read-only; never mutates state.

Open an interactive ChatGPT/MCP Apps dashboard for today's nutrition summary, safe meal estimation, and next-meal coaching. Read-only; logging still requires explicit user confirmation through existing tools.

Delete all local intake entries for a date after explicit user intent.

Delete all local hydration entries for a date after explicit user intent. Does not touch intake — pair with nourish_clear_day or use nourish_clear_day { include_hydration: true } for both.

Compute a per-nutrient diff between two days' summaries. Returns deltas (date_b - date_a) for calories, protein, carbs, fat, fiber, sugar, sodium plus what changed by meal type. Useful for 'how was today vs yesterday?' coaching.

Report local storage, fixture, USDA, and Open Food Facts readiness without returning secrets.

Summarize today, goal gaps, wearable context, and a safe next action for Telegram-style coaching.

Summarize local intake totals, confidence, and source coverage for a date. Pass `compare_to: 'yesterday'` or `compare_to: '7d_avg'` to add a `comparison` block with per-nutrient deltas — useful for trend coaching ('your protein is low again — third day in a row').

Decode a barcode from an image path, base64 image, or data URI without logging intake.

Delete a local intake entry by id. Gated: requires explicit user intent — agents must not call this autonomously.

Delete a single local hydration entry by id after explicit user intent.

Returns realistic example payloads of nourish_search_food, nourish_estimate_meal, and nourish_daily_summary so agents see the contract before any real call.

Estimate nutrition for a short meal text using local deterministic defaults. Accepts text or meal_text; preserve unresolved and confidence.

Estimate meal nutrition from an agent-provided photo observation; always requires user confirmation before logging.

Check late-day protein, calories, and hydration gaps with a compact Telegram-friendly next step.

Export local intake data as JSONL or CSV without provider secrets or tokens. Defaults to the 500 most-recent rows; pass since/until to scope by date or max_rows to widen/narrow. Omitted rows are reported so you can refine instead of dumping months of history into chat (use the `wellness-nourish export` CLI for a full unbounded dump).

Delete a local remembered meal by id or label after explicit user intent.

Fetch a USDA food by source_id, an Open Food Facts food by barcode source_id, or a TACO food by source_id.

Read local calorie, macro, and hydration goals.

Compute per-day progress vs configured goals (kcal, protein, carbs, fat, water) for today / yesterday / last_7_days / last_30_days. Returns per-day breakdown (consumed, goal, pct, delta_to_goal), period totals, multi-day averages, days_on_target count, and locale-aware next-action recommendations (pt-BR if profile language is Portuguese, otherwise en). Read-only: no logging side effects, no explicit_user_intent required.

Summarize local hydration for a date.

List local intake entries with optional filters: date OR since/until range, meal_type, tag, source_trace, min_confidence, limit. All filters AND together. Returns most-recent-first.

Read local remembered meals and nutrition preferences for personal Telegram shortcuts.

Log an intake entry only after explicit user intent. Pass explicit_user_intent: true after the user asks to save/log/register; accepts text or meal_text plus structured food data.

Log local hydration in milliliters after explicit user intent. Pass explicit_user_intent: true after the user asks to save/log water.

Lookup a packaged food barcode in Open Food Facts.

Decode a packaged-food barcode image, then lookup the product in Open Food Facts.

Returns the 11-question onboarding flow for the shared Delx Wellness profile. Read-only. The agent should ask these questions next so wellness-nourish (and the rest of the wellness stack) can personalize responses — non-secret data only, stored at ~/.delx-wellness/profile.json.

Suggest light pre-workout nutrition using goals, current intake, and optional WHOOP/Garmin context.

Describe local storage, secret handling, source licensing, and safety boundaries.

Returns the shared Delx Wellness profile (~/.delx-wellness/profile.json). Read-only. Surfaces calorie/macro targets, dietary preferences, restrictions/allergies, and goals so nourish coach/suggest tools can personalize meals.

Persist a partial patch to the shared Delx Wellness profile. Requires explicit_user_intent: true. Rejects any field containing oauth/token/secret/password/cookie/refresh/api_key/session — the profile is for non-secret wellness context only.

Read the most recent shared wellness_context (delx-wellness-context/v1) written by a wearable connector to ~/.delx-wellness/, so coach tools can be recovery/strain-aware without the agent passing it inline. Read-only; never fabricates wearable data. If no connector has persisted a context yet, returns available:false with the expected path. The returned context can be passed straight into nourish_daily_coach / nourish_suggest_next_meal / nourish_pre_workout_nutrition as wearable_context (or set auto_wearable:true on those tools to pull it automatically).

Personalized 3-step setup walkthrough for the human user. Adapts to current state (USDA key set? OFF enabled? local-dir writable?). Call this first when the user asks 'how do I use this?'

Save a personal meal shortcut locally after explicit user intent, for example 'meu cafe normal' -> '2 ovos e banana'.

Search food providers by query. Use taco or br_local for Brazilian staples, open_food_facts for packaged products, usda for generic foods, or all.

Set local calorie, macro, and hydration goals after explicit user intent. Use daily: {...} or flat shortcuts like calories_kcal/protein_g; pass explicit_user_intent: true after confirmation.

Suggest a next meal from today's intake, goals, personal memory, and optional wearable context.

Undo the most recently logged intake or hydration entry. The most common Telegram/agent recovery move ('I logged the wrong thing'). Returns what was undone so the agent can confirm. Requires explicit_user_intent. Pass kind: 'intake' | 'hydration' | 'any' (default 'any') to scope the undo.

Update a local intake entry by id. Quantity or grams_estimate changes rescale nutrients to keep summaries consistent. Gated: requires explicit user intent — agents must not call this autonomously.

Summarize seven days of local intake totals from a start date.