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govauctions.app

by app.govauctions

get_sold_comps

Read-only

Sold-price comps for a described item, computed across the full government-surplus market in the US, UK, CA or AU. Without an API key: coverage only (how many comparable sales, confidence, and a coarse price band). With an API key: the actual 25th/median/75th percentile final prices. Free keys at https://govauctions.app/developers. A miss says which kind it is: reason insufficient_comps (too few comparable sales yet) is worth retrying, reason category_not_priceable (miscellaneous, real-estate) never is — those categories cannot be priced from a title.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesItem description, e.g. '2015 Ford F-150'
limitNoHow many underlying sales to return with include=comps. Capped by plan.
stateNoOptional 2-letter state/region code for a regional range. Falls back to the national range when that state has too few sales; the response's `scope` field says which you got.
countryNoUS
includeNoSet to "comps" to also return the individual sales the range was computed from (title, final price, sale month, state). Requires a key; the number returned is capped by plan.
categoryNo

TDQS

A4.3/5.0
Behavior5/5

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

The description richly discloses behavior beyond the readOnly and openWorld hints: outputs differ by API key presence, miss reasons are categorized into retryable vs. non-retryable, and categories like real-estate cannot be priced. This gives the agent practical expectations for responses.

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 dense but every sentence carries useful information: what the tool does, key vs. no-key output, key acquisition, and miss semantics. It is front-loaded with the core purpose and avoids redundancy.

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?

With no output schema, the description covers the main return dimensions: coverage, confidence, price band, percentiles, and miss reasons. It could be slightly more explicit about the full response shape, but the combination of description and input schema gives an agent enough to call and interpret the result.

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 schema already documents most parameters, but the description adds meaning beyond the schema: it explains country scope, the role of an API key in output richness, and why certain categories (miscellaneous, real-estate) are unpriceable. It partially compensates for the undocumented 'category' parameter.

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 description states a specific action and resource: it computes sold-price comps for a described item across a defined market. It is clear and informative, but it does not explicitly distinguish itself from siblings like get_comp_coverage or get_sold_history beyond the core purpose.

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 gives clear usage context: explains key vs. no-key behavior, provides a link for free keys, and tells the agent which miss reasons are retryable and which are permanent. However, it does not explicitly direct when to choose this tool over its siblings.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes: search_listings and get_listing differ by lookup method, and get_sold_comps, get_sold_history, and get_price_trend are clearly separated by whether they provide aggregate percentiles, individual sales, or monthly trends. The only minor ambiguity is between get_sold_comps and get_price_trend, both derived from the same comps, but their descriptions make the difference in output format clear.

Naming Consistency5/5

All tool names follow the consistent verb_noun pattern using lowercase snake_case: get_* for data retrieval and search_listings for searching. There are no mixed conventions or style inconsistencies, making the naming highly predictable.

Tool Count5/5

With 8 tools, the server is well-scoped for its purpose of querying government auction listings and pricing data. Each tool covers a distinct functional need without redundancy, and the count is squarely in the ideal range for a domain-specific API.

Completeness4/5

The tool surface covers the core read-only workflows: searching and retrieving listings, getting sold-price comps, viewing historical sales, examining price trends, estimating flip value, and checking data coverage. A minor gap is the lack of explicit category or market listing endpoints, but agents can work around this via search_listings and get_comp_coverage.

Resources