Resolve Japanese food names to nutrition facts. All 2,538 foods from Japan's official tables.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ or use 1-click editor setup below.
๐ก Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
Japanese data tools for AI agents.
Japan has a wealth of clean, public, factual data โ food composition tables, corporate registries, laws, land prices. Almost none of it is usable by AI agents: no clean APIs, Japanese-only interfaces, Excel files behind web forms. Meanwhile, agents worldwide get asked to do Japanese tasks every day.
Torii Gate is the gateway: each dataset becomes a remote MCP server that any agent can discover, connect to, and call.
| Tool | Description | Endpoint |
|---|---|---|
| Japan Food DB | Resolve Japanese food names (fuzzy/colloquial forms included) to nutrition facts per 100g, based on the official Standard Tables of Food Composition in Japan. Covers foods USDA and Open Food Facts don't. | /mcp/food-db |
More tools are on the way โ corporate registry lookup, invoice registration checks, subsidy search. The platform is manifest-driven: adding a tool is one directory and one registry line.
Add the remote server to your client (Claude Code example):
Then ask things like "ใใปใใณใฎใตใฉใใใญใณใ็ใช้ถใใญ่ใฎใซใญใชใผใฏ?" โ the agent calls search_food / get_nutrition and gets factual data with sources attached.
No API key is needed for the free tier (rate-limited).
Smoke test:
src/tools/<name>/ and declares its MCP tools, pricing, and data sources in a manifest. llms.txt and MCP Registry server.json records are generated from manifests โ never hand-written.Full datasets are imported at deploy time and are not part of this repository.
This platform is built and operated almost entirely by AI agents โ planning, code, registry publishing, monitoring, and weekly funnel reports โ with a human in the loop only for publish/kill approvals and a budget review. It is an open experiment in whether an AI-operated business unit can find real agent-to-agent demand. Funnel numbers and decisions will be published as we go.
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