A
licenseA
qualityD
maintenanceKlarna-style product discovery for AI shopping agents. Makes product catalogs machine-readable so AI agents can search, compare, and purchase products programmatically.
6
MIT
Catalog Attribute Normalizer
by com.acjlabs
Server Details
Catalog attribute normalizer, taxonomy-grounded — no fabricated Google/Shopify category IDs.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
- Repository
- ACJLabs/catalog-normalizer
- GitHub Stars
- 2
- Server Listing
- Catalog Attribute Normalizer
TDQS
A4.7/5.0
Scored across 1 tool
Disambiguation5/5
Only one tool exists, so there is no potential for confusion between tools.
Naming Consistency5/5
With a single tool, naming consistency is perfect.
Tool Count5/5
One tool is appropriate for a focused normalizer server that performs a single batch operation.
Completeness5/5
The tool covers the full scope of catalog normalization including attribute canonicalization/extraction and category mapping for multiple taxonomies.
Available Tools
1 toolnormalize_catalogNormalize CatalogARead-onlyInspect
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| products | Yes | ||
| target_taxonomies | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| results | Yes |
TDQS
A4.6/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits: batch processing with independent per-product errors, controlled vocabulary for attributes with provenance (canonicalized vs. extracted), retrieval-grounded category paths with confidence and leaf_id, and fallback behavior. It aligns with annotations (readOnlyHint: true) and adds substantial context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is detailed but front-loaded with the core purpose. Each sentence contributes useful information (batch behavior, controlled vocabulary, provenance, confidence). A slight reduction for length, but the structure is logical and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (batch normalization, multiple taxonomies, confidence scores, error handling), the description covers all essential aspects: batch behavior, error reporting, vocabulary constraints, provenance, and interpretation of confidence and leaf_id. The output schema handles return values, so the description is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no descriptions (0% coverage), so the description compensates well by explaining the purpose of 'products' (title, description, raw_attributes) and 'target_taxonomies' (enum: google, shopify, amazon). It also clarifies output semantics, adding meaning beyond the schema structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool normalizes catalog products via attribute canonicalization/extraction and category-path mapping, specifying target taxonomies (google, shopify, amazon). It uses specific verbs and resources, effectively distinguishing from any potential siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (for normalizing batches of products) and provides guidance on interpreting results (e.g., treat extracted values as suggestions, low-confidence as needing human check). However, it lacks explicit when-not-to-use instructions, though the absence of siblings reduces the need.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
normalize_catalog
Related MCP Connectors
Classifies product listings into the official Google Shopping taxonomy (5595 categories). Output ...
- skulayerOAuthcom.skulayer
Preview-first feed optimization for retailers and agencies: audit, fix and serve product feeds.
Maps a product description onto the category path of a marketplace taxonomy.
AI-agent product catalog: search, lookup & purchase routing over verified merchant data.
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceEnables natural-language setup and management of a governed digital product catalog, including schema, products, and rules, with dry-run previews, audit logging, and two-phase confirmations for destructive actions.MIT
- AlicenseAqualityBmaintenanceStructured product data from the open web — where platform APIs don't reach. Schema.org + AI extraction. Pay per call via Stripe MPP.241 npm3Apache 2.0
- FlicenseAqualityBmaintenanceMCP server for autonomous catalog enrichment, turning raw ERP data into storefront-ready product listings with SEO titles, descriptions, images, schema.org, and validation. Provides tools for EAN lookup, image search, enrichment, batch processing, URL analysis, and Shopify payload generation.10-
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd
Glama MCP Gateway
Add one secure layer between your agents and this server.