Compare product prices across e-commerce platforms using Google Shopping and direct URL monitoring. Args: product_name: Product name to search product_urls: Direct product page URLs to check max_results: Max results (default 20)
| Name | Required | Description | Default |
|---|---|---|---|
| max_results | No | ||
| product_name | No | ||
| product_urls | 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 doesn't need to restate safety. However, it adds useful behavioral context by specifying the data sources (Google Shopping and direct URL monitoring) and the argument behavior, such as max_results defaulting to 20. This supplements the annotations without contradicting them, providing clarity on how the tool operates.
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?
The description is concise, with a clear purpose sentence followed by structured argument definitions. Every sentence earns its place, and there is no redundant or overly verbose content. The front-loading of the main action improves quick comprehension.
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 description covers the essential functionality and all parameters, but it does not explain the return value or output format. Since there is no output schema, the description should ideally state that the tool returns price comparisons or a list of prices across platforms. This omission leaves a gap for an agent expecting to know what to do with the result. Overall, it is minimally viable but missing output details.
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 0%, so the description must compensate. It does so with an 'Args' section that explains each parameter: 'product_name: Product name to search', 'product_urls: Direct product page URLs to check', 'max_results: Max results (default 20)'. This fully defines the parameters, making the tool usable without needing additional schema descriptions.
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's purpose: 'Compare product prices across e-commerce platforms using Google Shopping and direct URL monitoring.' This is a specific verb+resource construction that distinguishes it from sibling tools like search_amazon_products, which focuses on a single platform. The tool's scope is immediately evident.
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 implies usage by describing the mechanism (Google Shopping and direct URL monitoring) but does not explicitly state when to use this tool over alternatives. It lacks explicit 'use when' or 'use instead' guidance, though the purpose itself suggests a use case of multi-platform price comparison. The absence of explicit exclusions or alternative references makes this adequate but not exceptional.
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 targets a distinct data source and action: product price comparison, business registration lookup, Amazon product search, FDA recalls, and government contracts. There is no meaningful overlap; even the two product-related tools are clearly separated by scope (cross-platform vs. Amazon-specific).
All tool names follow a consistent verb_noun pattern in snake_case (compare_product_prices, lookup_business_registration, search_amazon_products, etc.). Though the verbs vary, the structure is uniform and predictable.
With exactly 5 tools, the surface is well-scoped for a data lookup server. Each tool covers a distinct domain without redundancy or bloat, making the set feel curated and manageable.
The server provides comprehensive read-only search coverage across its stated data domains: product pricing, business registry, Amazon catalog, FDA safety, and federal spending. There are no obvious gaps for a lookup-oriented tool set.