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postmarket_search

Search for AI/ML devices by postmarket criteria such as product code, panel, applicant, drift signals, recall count, or MAUDE trends. Returns per-device summaries with drift-signal counts.

Instructions

Find AI/ML devices by postmarket criteria — product code, panel, applicant, whether any drift signal exists, minimum recalls in 24 months, or a rising MAUDE trend. Returns per-device postmarket summaries with drift-signal counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
panelNoAdvisory panel, e.g. Radiology
applicantNoSubstring match on applicant/company name
product_codeNoFDA product code, e.g. QIH
maude_trend_upNoCode-level MAUDE trend above its own 3-year baseline
has_drift_signalNo
min_recalls_24moNoProduct-code recalls in trailing 24 months
signal_specificityNoRestrict the drift-signal filter to one tier: 'device' = attributable to this device (own line re-cleared, own applicant's recall, predicate neighbors) — the high-signal tier; 'product_code' = observed in its category
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description states the output includes per-device summaries with drift-signal counts, implying a read-only operation. However, it does not disclose behavioral traits such as authentication needs, rate limits, or handling of empty results. Since no annotations are present, the description carries the burden but provides minimal behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exceptionally concise at two sentences, front-loading the purpose and summarizing the output. Every phrase adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 8 parameters, no output schema, and no annotations, the description is somewhat incomplete. It does not specify how filters combine (AND/OR), pagination, sorting, or the structure of the return value beyond basic summaries. It covers the essentials but leaves practical usage details unexplained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 75%, so the schema already documents most parameters. The description lists some parameter types (e.g., 'minimum recalls in 24 months') but does not add significant meaning beyond the schema. It omits details on how parameters interact or default behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Find AI/ML devices by postmarket criteria' and lists specific searchable fields including product code, panel, applicant, drift signal, recalls, and MAUDE trend. It clearly distinguishes from sibling tools like device_postmarket_lookup and cohort_postmarket_stats by being a search tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. Sibling tools are listed in context, but the description itself lacks explicit usage instructions.

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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