get_warning_letterDirect lookup of an FDA warning letter. Accepts either the FDA reference number printed in the letter itself (e.g. "320-26-65") or the fda.gov URL slug ID for the letter.
| Name | Required | Description | Default |
|---|---|---|---|
| wl_number | Yes | FDA warning letter reference number (e.g. "320-26-65") or fda.gov URL slug ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only mentions accepted input formats but does not disclose whether it is read-only, authentication needs, rate limits, or what the return format looks like. This is a significant gap.
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 wasted words. The first sentence states the purpose, the second adds input details. Concise and front-loaded.
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 lookup tool with one parameter and no output schema, the description sufficiently explains the input format. It could mention that the result is the full warning letter content, but given the simplicity, it is reasonably complete.
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% and already explains the parameter. The description adds a concrete example ('320-26-65') and clarifies 'printed in the letter itself', which provides mild additional context but does not substantially improve understanding.
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 it is a 'Direct lookup of an FDA warning letter' and specifies the two accepted input formats (reference number or URL slug ID). This distinguishes it from sibling tools like search_enforcement (search) or get_company_history (history).
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 when you have a specific identifier, but does not explicitly state when not to use it or mention alternatives among sibling tools. Usage context is only implied by contrast with sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Tools are generally distinct, but get_company_history and search_enforcement both return warning letters and recalls, which could cause confusion if an agent needs a single record type. However, the entry points (company slug vs. keyword search) reduce ambiguity.
All tool names follow a consistent verb_noun pattern (e.g., get_company_history, list_recent_actions), using underscores and descriptive verbs like get, list, and search.
5 tools is well-scoped for a domain specializing in FDA enforcement data; each tool covers a clear use case without redundancy.
The set covers company history, recall by lot, warning letter lookup, recent list, and search, but lacks dedicated tools for direct recall by recall number or specific approval lookups, leaving minor gaps.