foodos-mcp
Related Servers
Alternatives to foodos-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceEnables AI assistants to calculate recipe and per-serving calories, macros, and FDA-rounded Nutrition Facts values from ingredient lines using USDA FoodData Central data, including scaling recipes and looking up ingredient nutrition.4MIT
- AlicenseAqualityCmaintenanceEnables AI assistants to convert recipe amounts between volume and weight for specific ingredients using measured densities, scale recipes, and convert oven temperatures, with source citations and alternative published values.4318 npmMIT
- AlicenseAqualityCmaintenanceEnables AI assistants to convert raw and cooked weights for meat and poultry by cut and cooking method from USDA data, calculate how much raw meat to buy for cooked servings, and look up cooking yields and sample ranges.5MIT
- AlicenseAqualityDmaintenanceEnables natural language access to USDA's FoodData Central database with 1M+ foods, supporting search, nutrition facts, food comparison, and daily value calculations.8MIT
- FlicenseAqualityBmaintenanceEnables LLMs to retrieve verified macronutrient data (calories, protein, carbs, fat) for real ingredients and quantities from USDA FoodData Central, with structured caveats for uncertain matches.8-
- FlicenseNot gradedqualityBmaintenanceEnables AI assistants to search recipes, compose nutritionally balanced meals, optimize weekly meal plans based on macro targets for family members, and generate consolidated grocery lists from a personal recipe database.-
TDQS
Scored across 12 tools
Most tools target distinct pipeline stages—food lookup, parsing, conversion, batch math, portioning, scaling, and shopping—so an agent can generally select by input and output. The only mild ambiguities are getFoodMacros vs lookupBarcode (both expose per-100g macros) and planBatchSize vs portionBatch (both reason about eaters), but the descriptions state the identifier/source and pre-cook/post-cook differences clearly.
All tool names use camelCase and most follow a clear verb+object convention: parseRecipe, searchFood, computeBatchMacros, scaleRecipe. shoppingList is a bare noun and toGrams uses a preposition rather than a verb, so the pattern is consistent in style but not perfectly uniform.
Twelve tools is an appropriate, well-scoped size for a food/macro meal-planning server. Each tool covers one distinct operation with no redundant clusters, and the set is large enough to span a full workflow without feeling bloated.
The server covers the complete recipe-to-shopping lifecycle: finding foods, parsing recipe and ingredient input, converting to grams, computing batch macros, accounting for cooked yield, portioning, scaling, and aggregating a shopping list. Dependent tools explicitly reference the outputs they need, and unresolved results return candidates instead of dead-ending.