[Premium Data] Search Amazon for product details, pricing, reviews, and ratings. Args: search_query: Product search query asins: List of ASINs to look up directly max_results: Max results (default 15)
| Name | Required | Description | Default |
|---|---|---|---|
| asins | No | ||
| max_results | No | ||
| search_query | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not contradict that. However, the description adds minimal behavioral context beyond the return value details. No mention of rate limits, pagination, or data freshness.
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?
Description is one sentence plus three argument lines, front-loaded with purpose. No redundant information. Every word is necessary.
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?
Given no output schema, the description hints at return values (product details, pricing, reviews, ratings). It covers essential input constraints. Could elaborate on interaction between search_query and asins, but adequate for a simple 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?
Input schema has 0% description coverage, so description must compensate. It provides brief explanations for each parameter: search_query ('Product search query'), asins ('List of ASINs to look up directly'), max_results ('Max results (default 15)'). This adds meaning beyond placeholder names.
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?
Description clearly states verb 'Search' and resource 'Amazon', and lists what it retrieves: product details, pricing, reviews, and ratings. This distinguishes it from sibling tools like 'compare_product_prices' and other search 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?
No explicit when-to-use or when-not-to-use guidance, but the purpose is implied from the name and description. Sibling tools such as 'compare_product_prices' exist, but the description does not contrast them.
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.
Many tools have overlapping functionality across categories (e.g., multiple search_arxiv, search_google_scholar, real estate tools, DNS/WHOIS checks). An agent would struggle to differentiate between similar tools from different categories, leading to ambiguity.
Tools follow a 'category__verb_noun' pattern mostly, but verbs vary (get, search, screen, check, etc.) and some categories use different orders (e.g., 'get_repo_stats' vs 'search_repos'). The consistency is acceptable but not uniform across the entire set.
With 152 tools, the server is excessively large for a single MCP server. While it aims to be an all-in-one gateway, the sheer number overwhelms the agent and likely exceeds practical limits for coherent selection.
The server covers a wide range of domains (finance, real estate, news, developer tools, etc.) but has notable gaps (e.g., social media APIs, CRM tools). Coverage is broad but not exhaustive, and some niche areas (e.g., global stock exchanges) are over-represented.