Calculate used-car market price statistics on arabam.com—count, min, max, median, and average price in TRY—for a brand or model across selected search pages.
Market stats for a brand/model: count, min, max, median and average price (TRY) across the first N search pages (pages: 1..5, default 3).
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | ||
| model | No | ||
| pages | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that results are computed over the first N search pages (1..5, default 3) and what metrics are returned, but it does not describe read-only safety, data freshness, performance/cost, or behavior when pages exceed available results.
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?
A single front-loaded sentence that packs metrics, scope, page range, default, and currency with no filler.
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 schema covers the returned statistics, and the description explains page scope and computed metrics. Still, for a tool with no annotations and 0% schema parameter descriptions, the missing brand/model optionality leaves an agent to infer important invocation behavior.
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 coverage is 0%, so parameter meaning must come from the description. It adds the useful pages constraint (1..5, default 3) and frames brand/model as filters, but it does not explain that brand/model default to empty strings or how empty values affect scope, leaving key semantics to inference.
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?
States a specific aggregation ('Market stats') over a brand/model and enumerates the exact metrics: count, min, max, median, and average price in TRY. This clearly differentiates it from sibling search, listing-detail, brand-list, and model-list tools.
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?
The aggregation purpose implies use when summary price statistics are wanted rather than individual listings, but it never explicitly contrasts with arabam_search or states when not to use it. It also omits whether brand/model can be blank.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.