Mood to meal
A user can type “rainy spicy biryani” or “light vegan salad,” and Moodish extracts intent tags, filters dietary constraints, ranks options, and explains the tradeoffs.
AI meal planning for Swiggy MCP
Food planning for the mood you are actually in.
Moodish turns cravings, budgets, dietary rules, and team lunch constraints into ranked meal shortlists with transparent AI reasoning and safe cart confirmation.
Real AI trace
See prompt, response, intent tags, and ranking scores.
Cart-safe by design
No checkout path exists without explicit confirmation.
Swiggy-ready boundary
Fixture data today, live MCP gateway when access is granted.
AI provider Uses deployed OpenRouter by default. Paste your own key to test without using mine.
Leave this blank to use the deployed OpenRouter configuration. Paste a key to override it for this browser session; Moodish does not store the key or print it in the recommendation trace.
Recommendation
Your shortlist
Choose a mode and plan a meal.
Product docs
What Moodish is proving
Office lunch mode
Teams can plan lunch by headcount, budget per person, cuisine rotation, and dietary rules. The app also models Instamart add-ons for fruit, drinks, snacks, and office extras.
Bring your own AI key
Reviewers can paste an OpenRouter key in Developer view. It is used only for that request, never displayed back, and not stored in recommendation memory.
Safe commerce layer
Moodish prepares a confirmed cart preview but does not place orders. Live Swiggy Food and Instamart MCP calls are isolated behind the gateway.
Cart review
No cart yet. Moodish will ask before building one.
Developer view
Taste memory
Open developer view to inspect memory.
Audit
Audit logs will appear here.
Recommendation trace
Run a recommendation to inspect mood parsing, ranking, and AI prompt/response.