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FoodEx2 Encoder

by FoodNotes-orgversion 1.0.0i★ –iRuns in the cloud
Registry nameorg.foodnotes/foodex2-encoder

Install

Overview

Description from the maker (original English)

Turn an English food description into an EFSA FoodEx2 code.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on the maker’s server: what you ask it goes through their server.
  • The registry lists no key to fill in.

You need

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Works in Claude on the web, desktop and mobile:

  1. Open Settings → Connectors
  2. Press Add custom connector
  3. Paste the address below and press Add
  4. If it asks you to sign in, sign in with the maker
SettingsConnectors1+ Add2https://…3
https://foodex2-encoder.foodnotes.org/mcp

Run this in your terminal

claude mcp add --transport http foodex2-encoder https://foodex2-encoder.foodnotes.org/mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "foodex2-encoder": {
      "url": "https://foodex2-encoder.foodnotes.org/mcp"
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "foodex2-encoder": {
      "type": "http",
      "url": "https://foodex2-encoder.foodnotes.org/mcp"
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

ChatGPT connects only to servers on the internet:

  1. Open ChatGPT on the web → Settings → Connectors (Apps)
  2. Press Create or Add (some plans need Developer mode under Advanced first)
  3. Paste the address below, give it a name and save
  4. If it asks you to sign in, sign in with the maker
SettingsConnectors1+ Create2https://…3
https://foodex2-encoder.foodnotes.org/mcp

Menu names can differ slightly by plan and over time.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

We have not read this server’s tool list yet; we check endpoints in rotation.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified against the domain foodnotes.org (the registry checks domain ownership)

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • … Not checked yet

    Endpoint responds

    Not checked yet; we check endpoints in rotation

  • … Not checked yet

    Sign-in with OAuth

    Not checked yet; we check endpoints in rotation

  • … Not checked yet

    No hidden instructions in tool descriptions

    Not checked yet; we read the tool list when we check the endpoint

Compared with others in Otheri

  • More stars than 0% of the other 32583 servers in this category
  • Published to the registry more recently than 64% of the other 32583 servers in this category
  • Basic checks equal to or better than 100% of the other 32583 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is FoodEx2 Encoder safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 21 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, sync. labs

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

foodex2-encoder

Library and stdio MCP server that turns an English food description into an EFSA FoodEx2 code (MTX catalogue): base term, facet descriptors, and labelled free text for what the code cannot express.

This repository is the core. A hosted version is available at https://foodex2-encoder.foodnotes.org/ .

Limitations

Check every code before you use it. In particular:

  • English input only, including foreign food names commonly used in English.
  • Group names the catalogue splits are kept as free text. When a description uses a name that the catalogue only has as several separate terms, that part goes to free text instead of the code. For example, *canned shellfish* is coded as *Canned seafood* with source-commodity=shellfish in free text, rather than *Canned seafood* made from crustaceans and molluscs. The code is not wrong, but it is broader than the description: *Canned seafood* also covers other marine animals, such as sea urchins.

What you need

  • Node.js 20+
  • Python 3 (one-time catalogue build from the vendored EFSA .ecf)
  • An OpenAI-compatible model endpoint and API key (OpenRouter is the default)

Install

git clone https://github.com/FoodNotes-org/foodex2-encoder.git

cd foodex2-encoder git submodule update --init npm install npm run build:catalogue npm run build:embeddings

data/ is generated locally (~25 MB) and is gitignored. Rebuild after updating the vendor/efsa-catalogues submodule.

The coding rules in src/rules/ (EFSA business rules, facet cardinality, our own checks) are shared with the FoodEx2 Atlas validator. Their data comes from rules/cardinality.yaml, rules/conflicts.yaml and vendor/efsa-business-rules/; after editing those, regenerate the committed src/rules/rules-data.json with npm run build:rules. Sources, licence and limits are in rules/efsa-business-rules.md.

Model endpoint

Copy .env (or export the same variables). OpenRouter needs only a key:

FOODEX2_LLM_API_KEY=sk-or-…          # or OPENROUTER_API_KEY

optional:

FOODEX2_LLM_BASE_URL=https://openrouter.ai/api/v1

FOODEX2_MODEL=openai/gpt-6.1-sol

Any other OpenAI-compatible endpoint works the same way (FOODEX2_LLM_BASE_URL, FOODEX2_LLM_API_KEY, FOODEX2_MODEL). The server calls that endpoint for classify / select / residual steps; your chat client’s subscription is separate.

Try it (CLI)

npm run cli -- encode "orange juice"

npm run cli -- encode "fried rice with chicken" npm run cli -- traverse "eggplant" # walk only (debug)

Lexical hits return in about a second. Descriptions that need a catalogue walk typically take 15–25 seconds.

Try it (MCP stdio)

npm run server

Point a client at that process. Cursor example (mcp.json):

{

"mcpServers": { "foodex2-encoder": { "command": "npx", "args": ["tsx", "/ABS/PATH/TO/foodex2-encoder/src/server.ts"], "env": { "FOODEX2_LLM_API_KEY": "sk-or-…" } } } }

Tool: encode. It returns the code, base term, facets, free text, fit, method, and a short explanation. Catalogue lookups, decoding, validation and EFSA guidance are in the free FoodEx2 Atlas MCP.

Tests and eval

npm test                 # unit tests (no model)

npm run lexical-eval # lexical base-term cases (no model) npm run encode-eval # end-to-end gold set (needs a model key)

eval/encode-base-term.yaml is a small, focused regression set: one realistic case per capability (catalogue synonym, model synonym, named dish, restraint, and so on). Gold rules can require or forbid facets and free-text fragments; expected may list several acceptable base codes (any-of).

npm run encode-eval records the model and MTX version, and on a complete run writes eval/results/encode-base-term.json for publishing or sharing with the public core repo. Always cite the model named in that file’s _provenance.

Layout

src/encode/     lexical → traverse → select → residuals

src/search/ lexical + embedding retrieval src/server.ts stdio MCP src/tools.ts tool handlers (shared surface for a future Worker wrapper) data/ generated catalogue + embeddings (local) eval/ gold cases + results artifact vendor/ EFSA catalogues (submodule)

Licence

MIT — see LICENSE. Copyright (c) 2026 e-accent BV.

That grant covers this software. EFSA catalogue files under vendor/efsa-catalogues/ (and generated data/ derived from them) remain subject to EFSA’s terms.

Attribution (eval inputs)

Some food names used as evaluation cases under eval/ were drawn from national food composition databases. We use food names only (not nutrient values). Those databases remain under their publishers’ terms:

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