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LiveDataLink

by io.github.blackboxfoundry

cpsc_recall_search

Read-onlyIdempotent

Search U.S. Consumer Product Safety Commission (CPSC) product recalls via SaferProducts.gov (keyless). Filter by product name/keyword, title, manufacturer, hazard, recall number, and date range. Returns recall number, date, title, products, hazards, remedy, manufacturers, injuries, and the official CPSC recall URL. Data: CPSC/SaferProducts.gov.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax recalls to return (default 25).
titleNoRecall title keyword filter.
hazardNoHazard keyword filter (e.g. 'fire', 'choking', 'laceration').
productNoProduct name filter (e.g. 'stroller', 'space heater').
date_endNoRecalls on/before this date (YYYY-MM-DD).
date_startNoRecalls on/after this date (YYYY-MM-DD).
manufacturerNoManufacturer name filter.
recall_numberNoExact CPSC recall number (e.g. '26561').

TDQS

A4.2/5.0
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 useful context by noting keyless access, naming the data source, and listing the exact fields returned. It does not discuss pagination or rate limits, but the annotation coverage lowers the burden.

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?

Three compact sentences cover the action, the configurable filters, and the return payload. There is no filler, repetition, or buried detail, and the key information is front-loaded.

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?

The description covers the data source, search filters, and returned fields well, especially given the absence of an output schema. It does not state default limit behavior, sort order, or pagination, which would make it fully self-contained for an agent.

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 each parameter already has a clear description and example. The tool description restates the filter categories in prose but does not add materially new parameter semantics 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 states a specific verb ('Search'), a specific resource ('U.S. Consumer Product Safety Commission (CPSC) product recalls via SaferProducts.gov'), and enumerates the filter dimensions. This clearly distinguishes it from sibling tools like cpsc_recent_recalls, which suggest a different scope.

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 description makes the usage context clear: use this tool when searching CPSC recalls by product name, title, manufacturer, hazard, recall number, or date range. It does not explicitly name alternatives or state when not to use them, so it stops short of a 5.

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.

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