Food Recipe MCP

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

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

No installation or configuration needed.

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

- "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 bymmarco-mMiniLMv2-L12-H384-v1.

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

- 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

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

- 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

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