How much sold-price data we hold per category for a market, and which categories can never be priced. Check this before building on the comps tools — it is the honest answer about what we cannot do.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | US |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint and openWorldHint annotations already covering safety, the description adds meaningful behavioral context: it reveals the tool's purpose is to expose limitations ('which categories can never be priced') and sets expectations about data coverage. It does not contradict 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?
Two concise sentences deliver purpose, scope, and usage guidance with no unnecessary words. Every clause earns its place, making it highly efficient.
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 tool is simple with one parameter, no output schema, and safe-read annotations. The description covers the core information, usage timing, and limitations. It could slightly benefit from hinting at the return structure, but the conceptual description is adequate for an agent to decide and invoke correctly.
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?
The schema has one parameter (country) with a clear enum and default, but the description does not explicitly mention 'country' or explain its role. It only refers to 'a market' indirectly. Since schema coverage is 0%, the description relies on the schema's self-explanatory enum, but it doesn't add extra meaning beyond the implicit market reference.
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 reports sold-price data coverage per category for a market and discloses which categories can never be priced. It uses a specific noun-phrase structure and distinguishes itself from sibling tools like get_sold_comps by focusing on coverage rather than actual comps.
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?
Explicitly advises to check this before building on the comps tools, and frames it as 'the honest answer about what we cannot do.' This gives direct when-to-use context and implies it should be used as a prerequisite before those tools.
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.
