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pangolinfo

Amazon All-in-One Scrape MCP

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filter_categories

Filter Amazon categories by sales, search volume, return rate, and other metrics to find high-potential categories or view detailed metrics for a single category.

Instructions

[Amazon category commercial-metrics filter] Filter categories by dozens of metrics (sales, GMS, search volume, conversion, return rate, price tier, competitor density, …) — or use as a "category detail" endpoint by passing a single categoryId. Use when: user says "find categories worth entering" / "high-sales categories" / "low return-rate categories" / "high search-volume but low competition categories" / "show me all metrics for category X"; category-level blue-ocean hunt; getting the 30+ metric snapshot of one category. Don't use: for niche-level (use filter_niches — finer granularity); for actual products in a category (use list_category_products); for just the readable name (use get_category_paths). Returns: data.items.data[{ id, categoryId, marketplaceId, timeRange, sampleScope, snapshotDate, unitSoldSum, glanceViewsSum, searchVolumeSum, netShippedGmsSum, buyBoxPriceAvg, buyBoxPriceTier, searchToPurchaseRatio, returnRatio, asinCount, offersPerAsin, newAsinCount, newBrandCount, avgAdSpendPerClick, unitSoldTrendDirection, unitSoldChangeRateBucket, ... trend + quantile-bucket fields }] + data.items.pagination.{ total, page, size, hasNext }. Pagination: use the 'page' param (default 1, 1-based, size capped at 10); 'pagination.hasNext=true' means more pages exist, 'hasNext=false' means last page. Pair with: ↑ required timeRange ('l7d' common) + sampleScope ('all_asin') + marketplaceId (defaults US); categoryId from search_categories / get_category_children; ↓ feed high-potential categories into list_category_products / list_bestsellers for real listings. Cost: ~1 point/page, ~5s. Tips: size capped at 10 (backend hard limit); only paginate when the user explicitly asks for more candidate categories — single-detail or quick-filter calls are fine on page 1; long-tail filter fields (unitSoldTrendDirections / metricChangeRateBuckets / dozens more) pass through via extraFilters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketplaceIdNoAmazon marketplace id. Common: US, UK, DE, JP, FR, IT, ES, CA. Defaults to US.US
timeRangeYesAggregation time range (required). Examples: 'l7d' (last 7 days — verified working). The exact enum is backend-defined; 'l7d' is the safest known value.
sampleScopeYesSample scope (required). Examples: 'all_asin' (all ASINs — verified working).
categoryIdNoWhen set, returns the full metric row for that single category (this endpoint doubles as the 'detail' endpoint). Omit to list multiple categories matching the filters. Example: '979832011'.
pageNoPage number, 1-based.
sizeNoPage size, max 10 (backend hard limit).
sortFieldNoSort field; any response field name is accepted (e.g. 'unitSoldSum', 'netShippedGmsSum').
sortOrderNoSort order: 'asc' or 'desc'.
unitSoldSumMinNoMin total units sold.
unitSoldSumMaxNoMax total units sold.
netShippedGmsSumMinNoMin total GMS (gross merchandise sales).
netShippedGmsSumMaxNoMax total GMS.
searchVolumeSumMinNoMin total search volume.
searchVolumeSumMaxNoMax total search volume.
buyBoxPriceAvgMinNoMin average buy-box price (marketplace currency).
buyBoxPriceAvgMaxNoMax average buy-box price.
buyBoxPriceTiersNoPrice-tier filter. Allowed: budget, mainstream, premium, luxury.
returnRatioLevelsNoReturn-rate quality buckets. Allowed: excellent, average, risk.
searchToPurchaseRatioLevelsNoSearch-to-purchase conversion buckets. Allowed: to_improve, average, excellent.
extraFiltersNoPass-through for any other upstream filter (e.g. unitSoldTrendDirections, newAsinCountLevels, metricChangeRateBuckets). Keys must match the upstream doc verbatim.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses cost (~1 point/page, ~5s), pagination limits, and extraFilters pass-through. No annotations, so description covers behavior adequately, though could explicitly state read-only nature.

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?

Well-structured with purpose upfront, then usage, don't-use, returns, pairing, cost, and tips. Slightly long but every sentence adds value; could be more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 20 parameters and no output schema, the description covers return structure, pagination, cost, and limitations. Additional info like example field names and pairing with other tools enhances completeness.

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?

All parameters have schema descriptions (100% coverage). Description adds context: categoryId dual role, timeRange examples, pagination limits, and extraFilters usage. Adds value beyond schema.

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?

Clearly states it filters categories by metrics or acts as a detail endpoint. Distinguishes from siblings like filter_niches and list_category_products.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly lists when to use (e.g., 'find categories worth entering') and when not to use, with specific alternative tools. Provides pairing and pagination tips.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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