find_predicatesList predicate device candidates for a 510(k) submission: recent clearances under an FDA product code, with review times and recall indicators. The starting set for predicate research.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max candidates (default 15, max 40) | |
| product_code | Yes | Three-letter FDA product code, e.g. NBW |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description usefully adds that the output includes review times and recall indicators, and that results are 'recent clearances'—but it does not explain what 'recent' means, whether recalls are simply flags or include details, or how ordering works. With annotations carrying the read-only signal, a 3 is fair.
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 crisp sentences with zero filler. The main purpose is front-loaded, and the supporting detail (review times, recall indicators, starting-set framing) is delivered in the second sentence. Every word 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 list/read tool with two parameters, full schema coverage, and read-only annotations, the description is nearly complete. It could add expected result behavior (e.g., ordering, default pagination, what 'recent' means), but these are minor gaps given the tool's low complexity and the annotations already cover safety. The output schema is absent, but the description's mention of review times and recall indicators partially compensates.
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 the semantic context that product_code is the seed for the predicate candidate search and that results are recent clearances, but it doesn't add anything beyond the schema for limit. Baseline 3 is appropriate because the schema does the heavy lifting.
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 opens with a specific verb ('List') and resource ('predicate device candidates for a 510(k) submission') and further narrows by mechanism ('recent clearances under an FDA product code, with review times and recall indicators'). It also distinguishes the tool as 'the starting set for predicate research,' which differentiates it from siblings like get_clearance or search_records. The title is also helpful.
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 clearly conveys when to use the tool: when beginning predicate research for a 510(k), using an FDA product code. It does not explicitly name sibling alternatives (e.g., get_clearance for a specific clearance, search_recalls for recall details, search_records for broader searches), so a direct when-not-to-use statement is absent. Still, the framing 'starting set for predicate research' gives a clear context and implies this is a scoping/candidate-listing first step.
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 clear primary purpose: AI device list, predicate candidates, monitoring feed, clearance detail, product code stats, recall search, and general full-text search. Minor overlap exists between find_predicates and product_code_stats (both product-code driven) and between search_recalls and search_records, but the descriptions distinguish specialized from general use.
Most tools follow a verb_noun pattern like find_predicates, get_changes, get_clearance, search_recalls, and search_records. Two names — ai_devices and product_code_stats — are noun phrases, creating a minor inconsistency, but the overall pattern remains readable and predictable.
Seven tools is well-scoped for a read-only FDA data server, covering lookup, search, statistics, monitoring, and specialized research use cases without unnecessary duplication. Each tool earns its place.
The set covers clearance details, recall search, product code statistics, change monitoring, predicate research, and the AI device list. There is no dedicated clinical trial tool, though search_records includes trials, and no device classification lookup, but these are workable gaps rather than dead ends.