Food Recipe MCP

<!-- mcp-name: io.github.AIDataNordic/food-recipe-mcp -->

Semantic search over 50,000+ food recipes — built for AI agents and LLMs. Two-stage hybrid retrieval (dense + sparse BM25, fused via RRF) with cross-encoder reranking. Supports natural language queries in Norwegian and English.

Live endpoint: https://recipes.aidatanorge.no/mcp Transport: streamable-http Demo: https://recipes.aidatanorge.no/

Connect

Add to your MCP client config:

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

Or with Claude Code:

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

Quick Test

Try the live demo in your browser: https://recipes.aidatanorge.no/

No installation or configuration needed.

MCP Tools

search_recipes

Semantic search over 50,000+ recipes from Food.com with hybrid retrieval and reranking.

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

Query examples:

• "Swedish meatballs with gravy"
• "healthy high-protein chicken bowl"
• "easy chocolate cake for beginners"
• "traditional Norwegian kjøttkaker"
• "hurtig pasta med kylling"

Search pipeline: Dense embedding (intfloat/e5-large-v2, 1024d) + sparse BM25, fused via Reciprocal Rank Fusion (RRF), reranked by mmarco-mMiniLMv2-L12-H384-v1.

ping

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

Data

• Source: Food.com (~50,000 recipes)
• Coverage: Wide range of cuisines, meal types, and cooking styles
• Nutritional data: calories, fat, protein, carbohydrates, sodium, fiber, sugar per serving
• Ratings: user rating + rating count per recipe
• Languages: English and Norwegian supported natively in queries

Architecture

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

Technical Stack

• Embeddings: intfloat/e5-large-v2 (1024d dense) + Qdrant/bm25 (sparse)
• Reranker: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
• Vector DB: Qdrant (self-hosted)
• Server: FastMCP 3.2 over HTTP
• Infrastructure: Ubuntu Server 24 LTS, Cloudflare Tunnel

License

MIT