mise-en-place
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- AlicenseNot gradedqualityDmaintenanceEnables AI agents to search the USDA's FoodData Central database and retrieve detailed nutritional information and ingredient lists. It supports comprehensive food data access through keyword searches and structured queries for specific food items.3MIT
- AlicenseAqualityDmaintenanceEnables natural language access to USDA's FoodData Central database with 1M+ foods, supporting search, nutrition facts, food comparison, and daily value calculations.8MIT
- AlicenseNot gradedqualityCmaintenanceProvides tools to search and retrieve USDA Food Data Central information, including food items, nutrients, and food groups, enabling AI agents to query food data through natural language.6 npmMIT
- FlicenseNot gradedqualityDmaintenanceProvides AI assistants with real-time access to nutrition data from USDA FoodData Central and FatSecret, enabling accurate answers with citations for nutrition queries.-
- AlicenseNot gradedqualityBmaintenanceEnables deterministic recipe and portion math with USDA-sourced macros, gram conversions, cooked yields, and per-eater portions.MIT
- FlicenseNot gradedqualityDmaintenanceProvides intelligent access to the USDA nutrition database through AI assistants, enabling users to search foods, compare nutritional content, find foods high in specific nutrients, and query authoritative nutrition data across 7,146+ food items through natural language.1-
TDQS
Scored across 8 tools
Each tool has a clearly distinct role: search_foods discovers candidate ingredients, get_nutrition and get_nutrition_batch retrieve verified nutrition data for one or many ingredients, save_override permanently corrects lookup data, and save_recipe/get_recipe/get_recipe_history/list_recipes manage the lifecycle of saved recipes. The boundary between fetching a food and fetching nutrition, or between correcting a lookup and saving a full recipe, is strongly reinforced by detailed descriptions.
Most tools follow verb_noun (search_foods, get_nutrition, save_recipe, list_recipes). Minor deviations: save_override is verb_verb rather than verb_noun, and get_nutrition_batch is a noun modifier rather than verb_noun. Mixed conventions, plus the label get_nutrition_batch when save/retrieve/recipes, however, are predictable and readable, so the set remains usable.
8 tools are correctly calibrated for a nutritional dataset and recipe store: 2 ingredient retrieval tools, batch mode, correction, recipe storage, and versioning. Each of the 8 tools is clearly justified and applicable, and complete omission is saved in the moderation of a useful compact-size dataset.
The core workflow is well covered: find ingredient, get nutritional info, batch retrieval, correct with override, save and read recipes, history and listing. The missing note is delete_recipe; save_recipe remains an append-only operation (each save creates a new version) with no way to remove old recipes. Which is a real limitation—a user can add recipes but cannot delete them. The current scope may be intentional, but this omission prevents entire recipe lifecycle management.