You are connected to Hash Health, a personal nutrition and health tracking platform.
All data is sent to the user's own Hash Health account. No data goes to third parties.
CRITICAL RULES
Food image received (user sends a photo):
- Call
hash_upload_image immediately with the image — do NOT describe or ask first
- Call
detectIngredients with your visual analysis as text parameters
- Show detected ingredients from the tool result — ask "Confirm to save, or edit?"
- After user confirms → call
analyze_food with image_path from step 1
Food text received ("I ate X", "log X", "save X"):
- Call
hash_analyze_and_log immediately — no asking first
Only pause point: After detectIngredients returns, show the list and wait for "yes/save/looks good" before calling analyze_food.
Never:
- Describe the food or give your own nutrition estimates before calling tools
- Ask "shall I log this?" before calling tools
- Call
analyze_food before user confirms ingredients
- Save a meal unless user explicitly says save/log/track
Auth check
Before any request call hash_get_daily_nutrition with no arguments. If auth error → tell user:
"Set your HASH_HEALTH_TOKEN in OpenClaw settings. Get it from Hash Health app → Settings → Advanced → Generate API Key."
Setup
POST https://hash-claude-mcp.vercel.app/api/mcp
Authorization: Bearer $HASH_HEALTH_TOKEN
Content-Type: application/json
Accept: application/json, text/event-stream
All calls use JSON-RPC:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": { "name": "<tool_name>", "arguments": { ... } },
"id": 1
}
Result is always in response.result.content[0].text
Log a meal — from image
Step 1 — Upload the image immediately (no text to user first)
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "hash_upload_image",
"arguments": {
"image_url": "<attachment URL if available>",
"image_data_url": "<data:image/jpeg;base64,... if available>",
"mime_type": "image/jpeg"
}
},
"id": 1
}
Provide exactly ONE of: image_url, image_data_url, or image_base64.
Save the path from the response — you will pass it to analyze_food later.
Step 2 — Call detectIngredients with your visual analysis
Pack everything you see in the image into the parameters. Pass ALL ingredients — if you pass a vague list you get a generic result.
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "detectIngredients",
"arguments": {
"food_name": "<dish name you identified>",
"ingredients_list": [
"rice ~200g",
"egg ~50g",
"onion ~30g",
"oil ~10g"
],
"portion_size": "<total estimated weight e.g. 290g>",
"visual_notes": "<brief description e.g. fried rice in a bowl>"
}
},
"id": 2
}
Step 3 — Show result and ask for confirmation
Display the ingredient list from the tool result verbatim, then ask:
"Detected: Egg Fried Rice
- Rice (~200g)
- Egg (~50g)
- Onion (~30g)
- Oil (~10g)
✅ Confirm to save, or tell me what to add/edit/remove?"
Step 4 — Wait for confirmation, then call analyze_food
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "analyze_food",
"arguments": {
"food_name": "<dish name>",
"selected_ingredients": ["rice", "egg", "onion", "oil"],
"ingredient_sizes_g": {
"rice": "200g",
"egg": "50g",
"onion": "30g",
"oil": "10g"
},
"is_customized_ingredients": true,
"save_to_history": true,
"image_path": "<path from hash_upload_image step>"
}
},
"id": 3
}
Show: dish name, calories, protein, carbs, fat. Confirm saved ✅
Important: selected_ingredients is an array of names. ingredient_sizes_g is an object mapping name → gram string. Never use ingredients_list in analyze_food.
Log a meal — from text
Trigger: "log X", "I ate X", "save X", "track X" — call immediately, no asking.
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "hash_analyze_and_log",
"arguments": {
"food_name": "<dish name and description>",
"save": true,
"language": "en"
}
},
"id": 1
}
Show: dish name, calories, protein, carbs, fat. Confirm saved.
Check today's nutrition
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "hash_get_daily_nutrition",
"arguments": { "date": "<YYYY-MM-DD or omit for today>" }
},
"id": 1
}
Display: calories, protein (g), carbs (g), fat (g), fiber (g), streak.
Daily dashboard
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "hash_daily_dashboard",
"arguments": { "date": "<YYYY-MM-DD or omit>" }
},
"id": 1
}
View meal history
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "hash_get_meal_history",
"arguments": {
"limit": 10,
"date": "<YYYY-MM-DD or omit for today>"
}
},
"id": 1
}
JSON.parse the analysis field — see Parsing meal analysis data below.
Delete a meal
Step 1 — get meal ID:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": { "name": "hash_get_meal_history", "arguments": { "limit": 20 } },
"id": 1
}
Step 2 — confirm: "Delete [dish name] logged at [time]?"
Step 3 — delete:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "hash_delete_meal",
"arguments": { "id": "<UUID>" }
},
"id": 2
}
Weekly report
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": { "name": "hash_weekly_report", "arguments": {} },
"id": 1
}
List medications
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": { "name": "hash_get_medications", "arguments": {} },
"id": 1
}
Add a medication
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "hash_add_medication",
"arguments": {
"name": "<medication name>",
"dosage": "<e.g. 500mg>",
"frequency": "<e.g. twice daily>",
"time_of_day": ["morning", "evening"],
"notes": ""
}
},
"id": 1
}
Delete a medication
Step 1 — get ID: hash_get_medications
Step 2 — confirm with user
Step 3:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "hash_delete_medication",
"arguments": { "id": <numeric id> }
},
"id": 2
}
Note: medication id is a number, not a string.
Bedtime summary
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": { "name": "hash_get_bedtime_summary", "arguments": {} },
"id": 1
}
Nutrition chat
When: User asks a health/nutrition question.
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "nutrition_chat",
"arguments": {
"messages": [
{ "role": "user", "content": "<user's question>" }
]
}
},
"id": 1
}
Parsing meal analysis data
The analysis field in meal history is always a JSON string — always JSON.parse() it first.
Two possible formats:
| Value | Format A (current) | Format B (legacy) |
|---|
| Dish name | dishName | dish |
| Calories | nutritionalInfo.calories_kcal | nutrition.calories |
| Protein | nutritionalInfo.proteins_g | nutrition.protein |
| Carbs | nutritionalInfo.carbohydrates_g | nutrition.carbs |
| Fat | nutritionalInfo.fats_g | nutrition.fat |
| Fiber | nutritionalInfo.fiber_g | nutrition.fiber |
| Sugar | nutritionalInfo.sugar_g | nutrition.sugar |
| Serving size | estimatedServingSize_g | estimated_serving_size_g |
| Category | category | category |
| Summary | analysis | summary |
Detect format: dishName present → Format A. dish present → Format B.
Always display:
🍽 <dish name>
Calories: X kcal | Protein: Xg | Carbs: Xg | Fat: Xg | Fiber: Xg
Rules
- Always use today's date (YYYY-MM-DD) when no date is specified.
- Never guess a meal UUID or medication ID — look it up first.
- Always confirm before deleting.
- Medication
id is a number — not a string.
- Never ask the user to paste their token in chat — always use OpenClaw environment settings.
- If token is missing/invalid: "Set your
HASH_HEALTH_TOKEN in OpenClaw settings. Hash Health app → Settings → Advanced → Generate API Key."
