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Sold prices for an item

sold_comps
Read-onlyIdempotent

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", a plain word (nwt, like new, excellent, very good, good, fair, poor), 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). Poshmark lists sold results by relevance, not date, so
        without it the sales can be years old; days=365 (or 90) keeps prices recent.
    limit: how many individual sales to include in `sales` (default 10, 0-50). Each sale has the same fields
        as a /v1/sold item.
    exclude_seller: comma-separated seller usernames to leave out (case-insensitive).
    sort: order of the included sales: "recent" (newest first), "price_asc" or "price_desc". Stats are unaffected.
    department: comma-separated: Women, Men, Kids, Home, Pets, Electronics. "Kids" also keeps kids' sizes.
    category: comma-separated Poshmark categories, e.g. "Jeans".
    include_removed: true also returns removed_items, the sales cleaning dropped, each with a removed_reason.
    deep: true reads up to ~190 sales (4 pages of up to 48, in Poshmark's relevance order) instead of one
        page (slower when not cached, about 10s; one lookup).
    source: "poshmark" (US, default) or "poshmark_ca" (Poshmark Canada; prices and fees in CAD).
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
deepNo
sizeNo
sortNo
limitNo
queryYes
sourceNoposhmark
categoryNo
conditionNo
departmentNo
exclude_sellerNo
include_removedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With readOnlyHint/idempotentHint/destructiveHint already declaring a safe, repeatable read, the description still adds real behavioral context: a busy/backoff response the caller must handle, the ~10s uncached cost and 4-page read for deep=true, that deep reads in relevance order, and that source=poshmark_ca returns CAD prices and fees. It does not describe pagination limits or the shape of the returned stats object, but the annotations carry the safety profile and the description goes beyond them.

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 lead sentence front-loads the returned statistics before any parameters, and the rest is an efficient parameter-by-parameter block. It is long, but nearly every clause adds actionable detail (defaults, ranges, edge cases) rather than filler; minor tightening is possible but nothing is clearly wasted.

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?

With no output schema and 12 parameters, the description must explain both the return payload and the inputs, and it does: it lists every statistic returned and documents each parameter. An agent has enough to construct a valid call and interpret the response without opening the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden for all 12 parameters, and it does: query format with examples and the kids'-clothing caveat, size exclusion warning, condition accepting plain words or exact grades, days range 1-365 with the relevance-sorting rationale, limit default 10 and 0-50 bound, comma-separated exclude_seller and department semantics, include_removed explaining removed_reason, and source currency behavior. This compensates completely for the undocumented schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence gives a specific verb+resource ("what an item actually sold for on Poshmark") and enumerates the concrete outputs: median sold price, p10-p90 range, days to sell, seller net after fees, price by condition, confidence rating, and matching sales. That is far more specific than a restated title. It stops short of a 5 only because it never names or contrasts with the sibling tool buy_or_pass, so the routing distinction has to be inferred from the name.

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?

It sets operational context ("call it for one item at a time") and a failure path ("if it reports busy, wait the given number of seconds and call again"), plus per-parameter usage counsel such as leaving size out unless the user named one and using days to keep prices recent since Poshmark sorts by relevance. It lacks any explicit when-not-to-use or alternative-tool comparison against buy_or_pass.

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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