What secondhand clothes, shoes and bags actually sold for on Poshmark, plus buy or pass verdicts.
Scored across 2 tools
buy_or_pass is a decision-layer tool while sold_comps is a raw data-retrieval tool, so an agent can tell which to call for a 'should I buy' vs 'what did it sell for' question. There is mild overlap since buy_or_pass internally consumes sold comps and accepts the same size/condition filters, but the output types (verdict vs statistics) are clearly distinct.
Both names use snake_case, so casing is consistent, but the conventions differ: buy_or_pass is an action/decision phrase while sold_comps is a noun phrase describing a dataset. Neither follows a verb_noun pattern, making the pairing readable but not predictable as a set.
Two tools is thin for a reselling assistant, though each is substantive rather than trivial and buy_or_pass layered on sold_comps is a coherent pairing. It sits at the borderline where an agent gets a decision plus its evidence but no exploratory surface.
The core loop of comps-to-verdict is covered, but there is no tool for active/competing listings or market supply, which materially affects a resale buy decision, and no way to save or revisit past evaluations. Filtering (size, condition, recency) is well covered, so gaps are notable rather than crippling.
Whether an item is worth buying at a given cost to resell on Poshmark: BUY, MAYBE or PASS with the reason, judged from its clean sold comps after Poshmark's fee. Call it for one item at a time; if it reports busy, wait the given number of seconds and call again.
query: brand + item, e.g. "patagonia better sweater".
cost: what you'd pay, e.g. the thrift-store tag price in USD.
min_profit: smallest profit after fees worth the effort (default 10).
size / condition: same filters as sold_comps.
source: "poshmark" (the only live marketplace today).
| Name | Required | Description | Default |
|---|---|---|---|
| cost | Yes | ||
| size | No | ||
| query | Yes | ||
| source | No | poshmark | |
| condition | No | ||
| min_profit | No |
What an item actually sold for on Poshmark: median sold price, typical range (p10-p90), days to sell, what the seller keeps after fees, price by condition, a confidence rating, and the matching sales. Call it for one item at a time; if it reports busy, wait the given number of seconds and call again.
query: brand + item, e.g. "coach tabby 26" or "lululemon align leggings", or a plain item type like
"womens jeans". For kids' clothing put girls, boys, kids, toddler or youth in the query ("girls jeans");
otherwise kids' sizes are removed.
size: only this size, e.g. "8", "M" or "10.5". Leave it out unless the user named a size: it can leave
very few sales.
condition: "new", "used", or an exact grade: new_with_tags, not_new_with_tags, used_like_new,
used_excellent, used_good, used_fair (comma-separated for several).
days: only sales from the last N days (1-365).
limit: how many individual sales to include (0-50).
deep: true reads up to ~190 recent sales instead of 48 (slower when not cached, about 10s; one lookup).
source: "poshmark" (the only live marketplace today).
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| deep | No | ||
| size | No | ||
| limit | No | ||
| query | Yes | ||
| source | No | poshmark | |
| condition | No |
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
sold_comps1 field changedInput schema / properties / deepAdded value: +{
+ "default": false,
+ "title": "Deep",
+ "type": "boolean"
+}buy_or_pass1 field changedInput schema / properties / sourceAdded value: +{
+ "default": "poshmark",
+ "title": "Source",
+ "type": "string"
+}sold_comps1 field changedInput schema / properties / sourceAdded value: +{
+ "default": "poshmark",
+ "title": "Source",
+ "type": "string"
+}buy_or_passsold_compsPoshmark Comps — sold-price search on the public Poshmark marketplace
Ask what a thrift find sells for. Real eBay sold comps, price range and net after fees.
GDPR-clean secondhand listings and sold comps for resale pricing research. No seller personal data.
Appraise resale items from photos and write listings for eBay, Mercari, OfferUp and more.
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