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nutrition-mcp

nutrition-mcp

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    • F
      license
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      quality
      D
      maintenance
      Enables natural-language nutrition research for Indian foods, combining a curated knowledge base with hybrid RAG and specialized agents. Provides MCP tools for knowledge search, safe calculations, document retrieval, and optional live web search.
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    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to log and edit meals, query a food journal, and retrieve calorie/macro/quality KPIs with server-side nutritional grading, plus manage staples and targets.
      1
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to analyze the nutrition of ingredients or recipe lines and to search recipe and food databases with optional diet, health, and cuisine filters.
      476 npm
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Provides 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
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    • A
      license
      B
      quality
      B
      maintenance
      Enables querying a global food nutrition database of generic and branded products, including search by name, lookup by barcode, and retrieval of resolved nutrient values pinned to a dated snapshot. Coverage spans USDA FoodData Central and Open Food Facts sources, with freshness health checks available.
      9
      AGPL 3.0

    TDQS

    A4.2/5.0

    Scored across 5 tools

    Disambiguation4/5

    The tools have largely distinct purposes: search_food resolves names to IDs, get_nutrition handles one food, compute_meal aggregates multiple items, find_foods ranks foods, and compute_targets estimates daily needs. The only minor overlap is get_nutrition versus compute_meal when a meal has a single item, but the descriptions clarify the intended boundary.

    Naming Consistency4/5

    All names use consistent snake_case with a verb_noun structure, which is readable and predictable. The verbs vary between search/find and get/compute, but the resource nouns make each tool's role clear.

    Tool Count5/5

    Five tools is well-scoped for a nutrition data server. Each tool covers a distinct operation—lookup, nutrient retrieval, meal aggregation, ranking, and daily targets—without obvious redundancy.

    Completeness4/5

    The surface covers the core workflows well: finding foods, getting nutrition, computing meals, ranking foods, and estimating targets. Minor gaps exist, such as no explicit ingredient-list tool or comprehensive vitamin/mineral target calculations, but agents can work around these.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues