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

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

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

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?'

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

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.

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.

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.

Lookup a packaged food barcode in Open Food Facts.

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

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

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

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.

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

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.

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

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

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

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

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

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

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

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

Update a local intake entry by id. Quantity or grams_estimate changes rescale nutrients to keep summaries consistent.

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.

Delete a local intake entry by id.

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

Delete a single local hydration entry by id 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.

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.

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.

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

Summarize local hydration for a date.

Read local calorie, macro, and hydration goals.

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.

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 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').

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.

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.

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

Export local intake data as JSONL or CSV without provider secrets or tokens.

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.