sakenowa-mcp 🍶

The first Model Context Protocol server for Japanese sake (日本酒) — a flavor-space engine, not an encyclopedia.

Give any MCP-capable LLM (Claude Desktop, Claude Code, …) a real sense of taste for sake. It wraps the open Sakenowa Data Project dataset — 1,700+ breweries, 3,200+ sake, 1,300+ six-axis flavor charts, monthly popularity rankings — and turns it into tools the model can reason with: search, profile, flavor-similarity recommendation, and side-by-side comparison.

As far as I can tell, there is no other sake MCP server in existence — existing "brewery" MCPs cover Western beer only. This one is built around what makes sake searchable: its flavor vector.

Why this exists

Ask an LLM "find me something like 八海山" and it guesses from training data. With sakenowa-mcp it does the real thing: it pulls 八海山's six-axis flavor vector and returns the nearest sake in flavor space — and can steer the search drier, lighter, richer, or to the deliberate opposite.

The positioning, in one line:

Sakenowa MCP = a flavor-space engine for sake. It does not store tasting notes or prices; it makes the shape of a sake's taste computable.

Related MCP server: Bar Assistant MCP Server

The flavor model

Every rated sake has six normalized axes (0–1):

Tools

Plus a recommend_sake MCP prompt as a friendly entry point.

Quickstart

Requires uv. Python is provisioned automatically (3.10+).

git clone https://github.com/mame0001/sakenowa-mcp.git
cd sakenowa-mcp
uv sync          # create venv + install
uv run pytest    # run offline tests

Add to Claude Code

claude mcp add sakenowa -- uv --directory /absolute/path/to/sakenowa-mcp run sakenowa-mcp

Add to Claude Desktop

In claude_desktop_config.json:

{
  "mcpServers": {
    "sakenowa": {
      "command": "uv",
      "args": ["--directory", "/absolute/path/to/sakenowa-mcp", "run", "sakenowa-mcp"]
    }
  }
}

Then ask: "Find me 3 sake similar to 久保田 but a bit drier," or "Compare 八海山 and 獺祭."

Try it without an LLM

uv run python -m sakenowa_mcp.demo   # full showcase: sync → search → profile → similar → compare
# or a one-liner:
uv run python -c "from sakenowa_mcp import data, search; ds=data.get_dataset(); print([b.name for b in search.search(ds,'八海山',5)])"

Configuration

Honest limitations

• ~41% flavor coverage (≈1,335 of 3,250 sake). Tools tell you when a bottle has no chart.

• No bottle specs. The dataset has no polishing ratio (精米歩合), rice variety, SMV/日本酒度, acidity, ABV, price, or junmai/ginjo grade. The tools say so explicitly so the model doesn't invent them.

• Estimated four-type class is a heuristic, self-calibrated against the dataset median — not the official SSI sensory category.

• Names are Japanese; romaji/kana search is best-effort substring matching.

Data & attribution

Sake data comes from the Sakenowa Data Project (さけのわデータプロジェクト). It is free and permits commercial use but requires attribution to "Sakenowa." Every tool response includes that attribution — please keep it intact. This project is not affiliated with or endorsed by Sakenowa.

License

Source code: MIT (see LICENSE). Sake data remains under the Sakenowa Data Project's terms.