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Estimate a selling price

estimate_price
Read-only

Turns comparable Vinted listings into a price recommendation with percentiles and a confidence level, so you can price items realistically.

Instructions

Turns comparable listings into a price recommendation: count, min, 25th percentile, median, average, 75th percentile, max, plus quick-sale / recommended / optimistic prices. Either pass comparables from search_similar_items, or pass the item facts and let it find them. It reports a confidence level and refuses to look precise when there are too few comparables - do not present a low-confidence number as a market price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoSize as printed on the label, e.g. "M", "42", "US 9.5".
brandNoBrand exactly as it appears on the item. Do not guess.
colorNoMain colour, in the storefront language where possible.
modelNoModel or product name, e.g. "Air Max 90", "iPhone 13".
notesNoAnything else worth telling a buyer, e.g. "original box included".
genderNo
countryNoVinted storefront to use (fr, be, de, at, es, it, nl, pt, …). Defaults to the server's configured country.
defectsNoEvery visible flaw: stains, scuffs, missing buttons. These are always kept in the description.
capacityNoStorage or volume, e.g. "128 GB", "50 ml".
categoryNoFree-text category guess, e.g. "sneakers", "winter coat".
currencyNo
materialNoMaterial, e.g. "cotton", "leather".
conditionNoVinted's five condition levels. Pick the one the photos actually support.
referenceNoReference / SKU / colourway code if visible on a label or the item.
comparablesNoComparables from a previous search_similar_items call. Omit to search automatically.
secondaryColorNoSecond colour, if the item is clearly two-tone.
Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint, but the description adds meaningful behavior: it autonomously searches for comparables if not passed, reports a confidence level, and deliberately 'refuses to look precise' with insufficient data. These traits go beyond the annotations and help the agent set expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences,front-loaded with the core function and output list, followed by a necessary caveat. No redundant words or vague jargon; every sentence earns its place.

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?

Given that there is no output schema, the description compensates by detailing the computed statistics and price tiers. It also explains both operating modes and confidence handling. However, it omits the exact response structure (e.g., how confidence is represented) and doesn't explicitly state the return format, so it falls just short of fully complete.

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

Parameters3/5

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

Schema description coverage is 88%, so parameters are mostly self-documenting. The description does add slight context by mentioning that 'comparables' should come from search_similar_items, but it doesn't elaborate on other parameters. With high schema coverage, the baseline of 3 is appropriate.

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's function with a specific verb ('Turns comparable listings into a price recommendation') and enumerates the exact outputs (count, min, 25th percentile, median, average, 75th percentile, max, quick-sale/recommended/optimistic prices). It is unmistakably distinct from siblings like search_similar_items (which finds comps) and update_price (which sets a price).

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?

The description explicitly provides two usage modes: pass comparables from search_similar_items, or pass item facts and let the tool find them. It also gives a clear when-not guideline: 'do not present a low-confidence number as a market price' when there are too few comparables. This is strong usage guidance with alternatives and a caveat.

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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