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Search FDA Import Alerts

fda_search_import_alerts
Read-onlyIdempotent

Search FDA Import Alerts by firm, alert number, red-list versus green-list status, country, keyword, or date. This is a stronger manufacturing and supplier-risk signal than one-off import refusals because it captures standing alert status and the specific firms currently listed under each alert.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (1-500)
offsetNoResult offset for pagination
countryNoCountry name
date_toNoEnd date for publish-date range (YYYY-MM-DD)
keywordNoKeyword to search in the alert title, reason, charge, or product notes
date_fromNoStart date for publish-date range (YYYY-MM-DD)
firm_nameNoFirm name (fuzzy match)
list_statusNoWhether the firm is on the red list or green list
alert_numberNoImport alert number, for example 66-40

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds valuable context about what the tool captures (standing alert status and specific firms currently listed), which deepens the agent's understanding beyond the annotations.

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?

Two sentences, each earning its place: the first lists search capabilities, the second provides strategic context. No redundancy or fluff.

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?

Although there is no output schema, the description gives an overview of the tool's purpose and hints at the returned entities ('specific firms currently listed under each alert'). Combined with the rich parameter descriptions, this is sufficient for a read-only search tool, though it doesn't describe pagination or result structure.

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?

All 9 parameters have descriptions in the input schema, providing 100% coverage. The description's list of search fields doesn't add detail beyond what the schema already provides, so the baseline 3 applies here.

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 uses a specific verb ('Search') and resource ('FDA Import Alerts') and enumerates the search dimensions (firm, alert number, red-list vs green-list status, country, keyword, date). It also contrasts this with one-off import refusals, effectively differentiating it from the sibling tool fda_import_refusals.

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

Usage Guidelines4/5

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

The second sentence explicitly positions this tool as a stronger manufacturing/supplier-risk signal than one-off import refusals, implying when to prefer it. While it doesn't name fda_import_refusals explicitly, the reference is clear enough, though it could go further by stating when to use the alternative.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes with clear boundaries, such as fda_search_drugs for drug applications and fda_search_510k for device clearances. However, some overlap exists, like fda_device_udi and fda_device_udi_lookup both querying UDI data, which could cause confusion despite differences in scope.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with a clear fda_ prefix, using descriptive verbs like search, get, list, and link. This uniformity makes the set predictable and easy to navigate, with no deviations in naming style.

Tool Count2/5

With 48 tools, the count is excessive for a single server, making it overwhelming and difficult for agents to manage. While the domain is broad (FDA data), the toolset feels bloated with many specialized or overlapping tools that could be consolidated.

Completeness5/5

The toolset provides comprehensive coverage of FDA data domains, including drugs, devices, inspections, compliance, recalls, and facilities. It supports full CRUD-like operations (e.g., search, get, link, save) and lifecycle workflows, with no obvious gaps for the intended purpose.

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