# Catalog Attribute Normalizer [Health: Active] **Category:** 💰 Finance & Fintech **Repository:** https://github.com/acjlabs/catalog-normalizer **GitHub Stars:** 2 **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/catalog-attribute-normalizer ## Description Normalizes messy catalog attributes, maps categories to Google/Shopify/Amazon. MCP + HTTPS API. ## Claude Desktop Quick Installation Remote MCP endpoint (confidence: high). Install path detected from listing signals. Add as a URL/SSE server in your client: ```json "mcpServers": { "catalog-attribute-normalizer": { "url": "https://catalog-normalizer-signup.acjlabs.com/#pro-intent" } } ``` ## Documentation & README # Catalog Attribute Normalizer **Taxonomy-grounded catalog attribute normalizer** — verified against the real Google Product Taxonomy, so it catches the plausible-but-wrong category IDs a generic LLM invents. Messy multi-source catalogs — titles, descriptions, images — in; consistent attributes, units, and grounded category mappings out. For merchant-ops teams and feed-tool developers wrangling a Shopify export, a supplier CSV, and a marketplace scrape that each spell "size" or "material" differently. ## What it does - Accepts a batch of raw products (`title`, `description`, `raw_attributes`), returns normalized attributes (canonicalized sizes/colors/units) plus category mappings for whichever taxonomies you request (Google Product Taxonomy, Shopify, Amazon). - Works as an MCP server (`normalize_catalog` tool) or a plain HTTPS API. ### Example (MCP tool call) ```json { "tool": "normalize_catalog", "input": { "products": [{ "title": "...", "description": "...", "raw_attributes": { "size": "Lrg", "Color": "Navy Blue" } }], "target_taxonomies": ["google", "shopify"] } } ``` ```json { "results": [ { "schema_version": "2.0", "source_title": "...", "category_paths": { "google": { "path": ["Apparel & Accessories", "Clothing", "Shirts & Tops"], "leaf_id": null, "confidence": 0.9 }, "shopify": { "path": ["Apparel & Accessories", "Clothing", "Tops"], "leaf_id": "aa-3-1", "confidence": 0.85 } }, "attributes": { "size": { "value": "L", "provenance": "canonicalized" }, "color": { "value": "navy", "provenance": "canonicalized" } } } ] } ``` `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). Each value's `provenance` is `"canonicalized"` when it came from your own `raw_attributes` input for that product (deterministic cleanup only, no recall) or `"extracted"` when the model inferred it from the title/description and it wasn't in your input — treat `"extracted"` values as a suggestion, the same way you'd treat a low-confidence `category_paths` entry. **Attributes are reliable by construction** — canonicalizing values already in your input, not recall. **Category classification is retrieval-grounded**, not recalled from memory: candidates are retrieved from the real, current Google and Shopify taxonomy files and offered to the model as suggestions, so a `leaf_id` almost always names a node that actually exists — measured against a 12-product evaluation set, each product checked against both the Google and Shopify taxonomies: **22 of 24 checks (91.7%) exact path + leaf-ID matches**. Still treat `confidence` and a `null` `leaf_id` as "worth a quick check," not a guarantee — a `leaf_id` is only ever returned when the path independently verifies against the real taxonomy file, so a `null` there is an honest "check this" signal, never a fabricated ID. Amazon has no comprehensive public taxonomy reference file to retrieve candidates from, so it stays best-effort (recall from memory) rather than retrieval-grounded. ## Pricing The free tier is self-serve today; paid plans are opening soon. - **Free** — 500 products/month, no card required. Self-serve today. - **Pay-as-you-go** *(opening soon)* — $0.01/product, no minimum. - **Pro** *(opening soon)* — $29/month for 5,000 products (~$0.0058/product effective). Want a paid plan now? [Register your interest](https://catalog-normalizer-signup.acjlabs.com/#pro-intent) and we'll set you up first. **What counts as one unit:** one product, not one call. A `normalize_catalog` call carrying 40 products uses 40 of your monthly allowance; batching is a convenience, not a discount. A batch is all-or-nothing — if it is larger than your remaining balance, the whole call is refused with `402` (the body names `required` and `remaining`) and nothing is classified, so you are never billed for a partial result that looks complete. A call that fails outright costs nothing. ## Getting a key The MCP server is live at `https://catalog-normalizer.acjlabs.com/mcp` (the alternate `https://acjlabs-catalog-normalizer.acjlabs.workers.dev/mcp` address reaches the same deployment and keeps working, so existing configurations need no change). Free-tier keys self-serve — `POST /v1/signup` with `{ "email": "you@example.com" }` returns your key directly in the response, good for 500 products/month, no card required. **Store it immediately — it is shown once and cannot be recovered if lost** (re-signup for a new one). See **** for the same steps plus pricing and a comparison against alternatives. Paid keys provision the same way via a one-time claim link once Pro/pay-as-you-go billing is live. ## Connecting it to an MCP client With Claude Code: ```bash claude mcp add --transport http catalog-normalizer \ https://catalog-normalizer.acjlabs.com/mcp \ --header "Authorization: Bearer YOUR_API_KEY" ``` Any other MCP client that supports a remote HTTP server with a custom header (Cursor, Cline, VS Code, etc.) works the same way: point it at the URL above with an `Authorization: Bearer YOUR_API_KEY` header. A gateway or scanner that can only forward the raw key value (no `Bearer` prefix) also works — both forms authenticate. Tool discovery (`initialize`/`tools/list`) doesn't require a key at all; only calling `normalize_catalog` does. See [docs/quickstart.md](docs/quickstart.md) for a full copy-paste walkthrough (getting a key, per-client configs, confirming the connection with `curl`) if you'd rather follow one linear guide. ## npm client Prefer a typed function over hand-rolling MCP JSON-RPC calls? [`@acjlabs/catalog-attribute-normalizer-client`](https://www.npmjs.com/package/@acjlabs/catalog-attribute-normalizer-client) (live on npm) wraps the `normalize_catalog` tool call: ```sh npm install @acjlabs/catalog-attribute-normalizer-client ``` ```ts import { createCatalogNormalizerClient } from "@acjlabs/catalog-attribute-normalizer-client"; const client = createCatalogNormalizerClient({ baseUrl: "https://catalog-normalizer.acjlabs.com", apiKey: "...", }); const results = await client.normalizeCatalog(products, ["google", "shopify"]); ``` ## Why not just use a category classifier? Category-classification APIs tell you *what* a product is. They don't touch the messier problem: standardizing *attributes* across sources that each spell them differently. The vendors that do take on the broader job are enterprise sales-led — demo request, annual contract, no self-serve signup and no public price. See the [full comparison](docs/comparison.md), or read [why "clean product data" is actually two different problems](docs/attribute-normalization-vs-classification.md) (canonicalization vs. classification, and why they fail differently). ## Source availability & support This repository hosts the documentation for the hosted service. The service implementation is not open source. Bug reports and feature requests are welcome in this repo's Issues; you can also reach us at .