Search FDA product recalls, safety alerts, and enforcement actions across drugs, food, and devices. Args: search_term: Product, drug, or company name category: Category: drug, food, device, or all severity: Classification: Class I, Class II, or Class III max_results: Max results (default 20)
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | all | |
| severity | No | ||
| max_results | No | ||
| search_term | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe read operation that may return varying results. The description adds the scope (across drugs, food, and devices) but does not disclose additional behavioral traits such as pagination, rate limits, or return format. This is adequate but not rich.
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 and well-structured: a one-sentence purpose followed by a clean Args list. Every line is informative and there is no redundant or fluff content.
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 search tool with four parameters, the description covers the essential scope and parameter meanings. It does not describe the return format, but since there is no output schema and the tool is a fairly straightforward search, this is a minor gap. Overall, it is complete enough for an agent to 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 provides only types and defaults with 0% description coverage. The description compensates fully by explaining each parameter: search_term as product/drug/company name, category with allowed values (drug, food, device, all), severity with classifications (Class I, II, III), and max_results with default. This adds significant meaning beyond the schema.
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 searches FDA product recalls, safety alerts, and enforcement actions across drugs, food, and devices. This is a specific verb+resource combination and is distinct from sibling tools which focus on prices, business registration, Amazon products, and government contracts.
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 provides clear context that this tool is for FDA recall and safety information. However, it does not explicitly mention when not to use it or alternative tools, though the sibling tools are topically distinct enough that the intended usage is fairly unambiguous.
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.