Live market price distribution for a product from current eBay Buy It Now listings: median, p25, p75, min, max, and sample size. Use to answer 'what is this worth right now'.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Product to price | |
| min_price | No | Floor to exclude junk/parts listings, default 25 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It states that the data is live, sourced from current eBay Buy It Now listings, and includes sample size, which is useful context. It does not mention rate limits, failure modes, or whether network calls are made, but for a simple read-only price lookup the disclosed behavior is adequate.
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 sentences with no fluff. The first sentence front-loads the core output and data source, and the second gives the practical usage context. Every clause earns its place.
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?
For a simple two-parameter tool, the description explains the return value composition despite lacking an output schema, and the schema covers parameters fully. It omits edge-case behavior like empty results or errors, but those are minor for this use case.
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 100%, so the schema already documents both parameters. The description adds little beyond naming the product and implying a price floor; it does not enrich the parameter semantics beyond what the schema provides.
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 identifies a specific verb-resource pair: it computes a live market price distribution from current eBay Buy It Now listings. It lists concrete outputs (median, p25, p75, min, max, sample size) and a usage phrase, but it does not explicitly differentiate itself from sibling tools like flip_margin or scan_deals.
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 description gives an explicit use case: 'Use to answer what is this worth right now.' This provides clear context for when to invoke the tool, though it does not state when not to use it or name alternatives among the siblings.
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.
Each tool has a clearly distinct role: hot_list provides a cached, curated list of pre-scored deals, scan_deals runs a live product-specific scan, price_check returns market price distribution, and flip_margin estimates profit and ROI. There is no meaningful overlap that would cause an agent to pick the wrong tool.
Names are readable and share a lowercase_snake_case style, but the grammatical pattern is mixed. scan_deals and flip_margin follow a verb_noun pattern, while price_check and hot_list are noun/adjective phrases, so the naming convention is not fully consistent.
Four tools is an appropriate, focused size for a market scan server. Each tool earns its place and covers a distinct part of the workflow without unnecessary bloat or missing essentials.
The set covers the main market-scan lifecycle: discover curated deals via hot_list, scan specific products via scan_deals, verify market value via price_check, and evaluate profitability via flip_margin. There are no obvious dead ends for the server's stated purpose.