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Return machine-detected FDA record changes for monitored product codes since a specified date, including latest category snapshot and postmarket coverage.

Instructions

Return machine-generated FDA public-record changes detected for monitored product codes since a caller-supplied date, plus each code's latest category snapshot and postmarket coverage. Defaults to Constat Radar's five-code watchlist and the last seven days. Analyst verdict text and internal review status are excluded; use next_since as the next polling cursor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum changes to return (default 100).
sinceNoReturn changes on or after this ISO date or UTC timestamp. Defaults to the last seven days; pass the prior response's next_since for exact polling.
product_codesNoFDA product codes to poll. Omit for the default Radar watchlist.
Behavior4/5

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

With no annotations, the description fully discloses that the tool returns only machine-generated changes, excluding analyst verdict and internal review status. It also explains the default behavior for product_codes and since, and hints at pagination via next_since. No destructive or hidden behaviors are omitted.

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: two sentences that front-load the core purpose and immediately follow with defaults, exclusions, and usage guidance. Every sentence earns its place without any unnecessary detail.

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

Completeness4/5

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

Despite lacking an output schema, the description adequately outlines what is returned (changes, category snapshot, postmarket coverage) and what is omitted (analyst verdict, review status). Combined with the polling hint about next_since, this provides sufficient context for an agent to invoke and process results.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by stating defaults ('Constat Radar's five-code watchlist and the last seven days') and the purpose of the since parameter for polling (use next_since). This provides context beyond the schema's field descriptions.

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 it returns 'FDA public-record changes detected for monitored product codes since a caller-supplied date, plus each code's latest category snapshot and postmarket coverage.' This clearly identifies the verb (return), resource (changes and snapshots), and scope (monitored product codes), distinguishing it from sibling tools that perform static lookups or searches.

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

Usage Guidelines3/5

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

The description mentions that analyst verdict and review status are excluded and that next_since should be used as a polling cursor, providing some usage context. However, it does not explicitly guide when to use this tool over specific siblings like device_risk_lookup or firm_compliance_history, leaving the selection to the agent's inference.

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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