Catalog Attribute Normalizer

Catalog attribute normalizer, taxonomy-grounded — no fabricated Google/Shopify category IDs.

CommunityOnlineNo sign-inGlobalFreeRead-only

What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Catalog attribute normalizer, taxonomy-grounded — no fabricated Google/Shopify category IDs.

    How to connect

    How to connect Catalog Attribute Normalizer to Claude

    The server address to paste into Claude settings:

    https://catalog-normalizer.acjlabs.com/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect Catalog Attribute Normalizer to ChatGPT

    The server address to paste into ChatGPT settings:

    https://catalog-normalizer.acjlabs.com/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect Catalog Attribute Normalizer to Cursor

    The server address to paste into Cursor settings:

    https://catalog-normalizer.acjlabs.com/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (1)

    Raw names from tools/list. Only developers need these.

    normalize_catalogNormalizes a batch of catalog products (attribute canonicalization/extraction + category-path mapping into the requested target taxonomies: google, shopify, amazon). Returns one result per input product, same order: a NormalizedProduct on success, or { error, source_title } if that specific product's classification failed — one product's failure never voids the rest of the batch. attributes is keyed by a controlled vocabulary (size, color, material, gender, sleeve_length — unrecognized keys are dropped, not passed through under a model-chosen name) and each value carries provenance: "canonicalized" means it came from your own raw_attributes input for that product (deterministic cleanup only, no recall); "extracted" means the model inferred it from the title/description and it wasn't in your input — treat extracted values as a suggestion, not a confirmed fact about the product, the same way you'd treat a low-confidence category_paths entry. category_paths for google and shopify is retrieval-grounded against the real, current taxonomy files (not recalled from memory) — measured at 22/24 (91.7%) exact path+leaf_id matches on a 12-product evaluation set; amazon has no comparable public reference file, so it stays best-effort. Each entry's confidence (0-1) and leaf_id (null when not confident it matches a real node) are the honest signal regardless of taxonomy — treat a low-confidence or null-leaf_id result as a suggestion worth a quick human check, not a confirmed classification.