Month-by-month median sale price for an item, plus the first-to-last change — 'is this category softening?'. Computed from the same comps as get_sold_comps, so it never contradicts that range. Returns an empty series when too few months clear the sample threshold.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Item description, e.g. 'forklift' | |
| state | No | Optional 2-letter state/region code. | |
| country | No | US | |
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavior beyond that: the empty-series behavior when sample threshold is not met, and the consistency guarantee with get_sold_comps (never contradicts that range). No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: the first states the core output, the second contextualizes data source and consistency, the third explains a caveat. Front-loaded and free of fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output behavior is well specified (monthly series, first-to-last change, empty series) given no output schema. It notes the sibling relationship and the source dataset. It does not explain how parameters like category affect results, but the core tool behavior is sufficiently complete for a read-only trend tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%, with q, state, and country already described in the schema. The category parameter lacks a schema description and the tool description does not compensate by explaining it or any other parameter details. The description adds no parameter-specific meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a month-by-month median sale price and first-to-last change, answering a specific question ('is this category softening?'). It distinguishes itself from get_sold_comps by noting it uses the same comps but for trend analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides context for when to use the tool (trend/softening analysis) and explicitly ties it to get_sold_comps, implying a complementary relationship. However, it does not explicitly say when not to use it or point to alternatives for specific scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
