MCP server

Additive Atlas

space.monon/additive-atlas

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Food additive regulations of Japan, the EU and the US, side by side, with sources (free tier).

Website

At a glance

Type

MCP server

Publisher

monon.space (verified by the MCP Registry)

Status

Working at last check

Tools

5

Token cost

About 1,577 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http additive-atlas https://api.additiveatlas.monon.space/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.additive-atlas]
url = "https://api.additiveatlas.monon.space/mcp"

Gemini CLI

{"mcpServers":{"additive-atlas":{"httpUrl":"https://api.additiveatlas.monon.space/mcp"}}}

Cursor

{"mcpServers":{"additive-atlas":{"url":"https://api.additiveatlas.monon.space/mcp"}}}

VS Code

{"servers":{"additive-atlas":{"type":"http","url":"https://api.additiveatlas.monon.space/mcp"}}}

Antigravity

agy mcp add additive-atlas https://api.additiveatlas.monon.space/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"additive-atlas","server_url":"https://api.additiveatlas.monon.space/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"additive-atlas","server_url":"https://api.additiveatlas.monon.space/mcp"}

Tools

• analyze_label ~432 tokens
  Find the additives named on a label (same as HTTP POST /v1/labels/analyze). Splits the additive section into tokens and returns, per token, the substances it resolves to (SubstanceSummary: facts on their own axis), plus known interactions between the detected substances. Cutting the additive section
• analyze_label_diff ~300 tokens
  (Paid; not available through MCP.) Same input as analyze_label, but each detected substance carries the cross-axis derivation. This tool does not return that data: it returns the HTTP endpoint (POST /v1/labels/analyze/diff with the same JSON body, 0.01 USDC per request via x402 on Base) to call inst
• get_substance ~295 tokens
  One substance with everything its own roster says (same as HTTP GET /v1/substances/{id}): identifiers, category, aliases, translations, the source-ledger row, groups, and — depending on the axis — Japanese use standards, EU/Codex conditions of use, mandatory labelling statements, the roster's defini
• get_substance_diff ~207 tokens
  (Paid; not available through MCP.) For every axis other than the substance's own, what that axis says (with a per-cell source) kept separate from what was derived from it; when nothing can be concluded, a refusal says why and what would settle it. This tool does not return that data: it returns the
• search_substances ~343 tokens
  Look up substances by name on one regulatory axis (same as HTTP GET /v1/substances?name=…). The name is normalised (NFKC, whitespace removed, lower-cased) and matched **exactly** against the names and aliases the axis itself uses in that language (E numbers and official names for EU, 品目名 and label t

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