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LiveDataLink

by io.github.blackboxfoundry

recall_screen

Read-onlyIdempotent

One-call product-safety recall sweep across CPSC (consumer products), openFDA (drug/device/food enforcement), and NHTSA (vehicles). Provide a product/keyword/manufacturer query and/or a full vehicle (year+make+model). Results are normalized, deduped within and across sources, severity-rolled (FDA Class I or death-related = high), and summarized with a by-classification breakdown. A source that fails is noted, not fatal. Premium cross-source synthesis. Verify against the official sources before acting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoProduct, keyword, or manufacturer to screen (e.g. 'infant formula', 'Acme Corp').
sinceNoOptional lower-bound date (YYYY-MM-DD) for FDA recalls.
domainsNoOptional subset of sources to check; default checks all applicable.
vehicle_makeNoVehicle make (e.g. 'Toyota').
vehicle_yearNoVehicle model year (required with make+model for NHTSA).
vehicle_modelNoVehicle model (e.g. 'Camry').

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable operational context: results are normalized, deduped, severity-rolled, summarized, and source failures are non-fatal. It also includes an important verification caveat. This goes well beyond the structured annotations without contradicting them.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and each sentence contributes behavioral or safety guidance. Phrases like 'Premium cross-source synthesis' and 'one-call' add some promotional tone, but the overall structure is compact and scannable.

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?

Given the moderate complexity of six optional parameters and no output schema, the description covers the key invocation requirements: input choices, normalization, severity classification, failure handling, and the need to verify against official sources. It does not detail exact response fields, but that is not critical for selecting and calling this tool.

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 100%, so parameters are already well documented. The description reinforces that a product/keyword/manufacturer query and/or full vehicle identifiers are expected, but it does not add significant meaning beyond the schema.

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 strong, specific verb ('sweep') and names the exact resources covered (CPSC, openFDA, NHTSA). It clearly differentiates itself from single-source siblings like cpsc_recall_search and fda_drug_recalls by emphasizing cross-source aggregation and one-call coverage.

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 implies when to use this tool: when a multi-source recall sweep is needed. It tells the agent what inputs to provide ('query and/or a full vehicle'), but it does not explicitly name alternatives or state when a single-source tool should be preferred over this aggregate.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources