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AIDataNordic

food-recipe-mcp

by AIDataNordic
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Python
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Food Recipe MCP

针对 50,000 多条食谱的语义搜索——专为 AI 智能体和 LLM 构建。采用两阶段混合检索（密集 + 稀疏 BM25，通过 RRF 融合）并结合交叉编码器重排序。支持挪威语和英语的自然语言查询。

实时端点： https://recipes.aidatanorge.no/mcp
传输方式： streamable-http
演示： https://recipes.aidatanorge.no/

连接

添加到您的 MCP 客户端配置中：

{
  "mcpServers": {
    "food-recipe": {
      "type": "streamable-http",
      "url": "https://recipes.aidatanorge.no/mcp"
    }
  }
}

或者使用 Claude Code：

claude mcp add --transport http food-recipe https://recipes.aidatanorge.no/mcp

Related MCP server: Food MCP Server

快速测试

在浏览器中尝试实时演示：
https://recipes.aidatanorge.no/

无需安装或配置。

MCP 工具

search_recipes

通过混合检索和重排序，对来自 Food.com 的 50,000 多条食谱进行语义搜索。

search_recipes(
    query="quick Italian pasta for weeknight dinner",
    diet="vegetarian",      # vegetarian | vegan | gluten-free | dairy-free | low-carb | keto | paleo
    max_minutes=30,         # maximum total cooking time in minutes
    difficulty="easy",      # easy | medium | hard
    limit=5                 # default 5, max 20
)
# Returns: rerank_score, rrf_score, title, description, total_time, difficulty,
#          diet, main_ingredient, servings, ingredients, instructions, nutrition,
#          rating, rating_count, source, recipe_id

查询示例：

  • "Swedish meatballs with gravy"

  • "healthy high-protein chicken bowl"

  • "easy chocolate cake for beginners"

  • "traditional Norwegian kjøttkaker"

  • "hurtig pasta med kylling"

搜索流水线： 密集嵌入 (intfloat/e5-large-v2, 1024d) + 稀疏 BM25，通过倒数排名融合 (RRF) 进行融合，并由 mmarco-mMiniLMv2-L12-H384-v1 进行重排序。

ping

ping(name="world")
# Returns: "Hello world! Recipe MCP server is running."

数据

  • 来源： Food.com（约 50,000 条食谱）

  • 覆盖范围： 广泛的菜系、餐点类型和烹饪风格

  • 营养数据： 每份的热量、脂肪、蛋白质、碳水化合物、钠、纤维、糖分

  • 评分： 每条食谱的用户评分 + 评分数量

  • 语言： 查询原生支持英语和挪威语

架构

Food.com recipes → Python ingest → Qdrant (recipe_data_v2 collection)
                                         ↓
                              Hybrid search (dense e5-large-v2 + sparse BM25)
                                         ↓
                              RRF fusion + cross-encoder reranking
                                         ↓
                              FastMCP 3.2 → MCP clients / AI agents

技术栈

  • 嵌入： intfloat/e5-large-v2 (1024d 密集) + Qdrant/bm25 (稀疏)

  • 重排序器： cross-encoder/mmarco-mMiniLMv2-L12-H384-v1

  • 向量数据库： Qdrant (自托管)

  • 服务器： 基于 HTTP 的 FastMCP 3.2

  • 基础设施： Ubuntu Server 24 LTS, Cloudflare Tunnel

许可证

MIT

Install Server
A
license - permissive license
A
quality
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
1Releases (12mo)
Commit activity

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Tools

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