Catalog Attribute Normalizer

com.acjlabs/catalog-normalizerVersion 0.1.2

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

Status

Responds1272 msNov 14, 2026, 13:45 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 13:45 UTC
Latency: 1272 ms
Remotes responding: 1 of 1
Sign-in: No sign-in
Hosting: Hosted outside the EU (detected)
Registry data as of: Nov 14, 2026, 22:14 UTC

Data from the official MCP Registry, checked by run.zero. How we check

Connect

Streamable HTTP

https://catalog-normalizer.acjlabs.com/mcpResponds1272 msNov 14, 2026, 13:45 UTC

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Claude Code

claude mcp add --transport http catalog-normalizer https://catalog-normalizer.acjlabs.com/mcp

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: catalog-normalizer
Remote MCP server URL: https://catalog-normalizer.acjlabs.com/mcp

Cursor

{
  "mcpServers": {
    "catalog-normalizer": {
      "url": "https://catalog-normalizer.acjlabs.com/mcp"
    }
  }
}

VS Code

code --add-mcp '{"name":"catalog-normalizer","type":"http","url":"https://catalog-normalizer.acjlabs.com/mcp"}'

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Tools

1 tools

Tools of Catalog Attribute Normalizer
	Description		Read-only	Idempotent	Open world
`normalize_catalog`Normalizes 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.	Normalizes 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.	Not declared	Yes	Not declared	No

Details

Registry status: Active
Published: Jul 31, 2026
Updated: Jul 31, 2026
Website: https://acjlabs.com/products⁠ (opens in a new tab)
Repository: https://github.com/acjlabs/catalog-normalizer⁠ (opens in a new tab)
Categories (suggested): E-commerceSuggested categories are assigned automatically from the registry text and may be wrong. Verified operators can correct them.
GDPR: Not declared

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