Return the most recent U.S. CPSC product recalls (date-sorted, newest first) via SaferProducts.gov (keyless). Looks back a configurable window (default 90 days). Each result links to the official CPSC recall page. Data: CPSC/SaferProducts.gov.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of recent recalls to return (default 10). | |
| lookback_days | No | How many days back to scan (default 90). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior, so the description's addition of 'keyless' access, the SaferProducts.gov source, configurable lookback, and the fact that each result links to the official CPSC page adds meaningful behavioral context 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is front-loaded and information-dense, and the rest is brief. There is minor redundancy in mentioning 'via SaferProducts.gov' and later 'Data: CPSC/SaferProducts.gov,' but overall every sentence contributes useful detail about behavior, source, or defaults.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only list tool with no required parameters, the description covers source, authentication posture, ordering, lookback default, and what each result provides. It does not enumerate result fields, but the absence of an output schema is partially mitigated by stating that results link to official recall pages.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both 'limit' and 'lookback_days' already described with defaults. The description adds only a general mention of a configurable window and the default 90 days, so it does not substantially improve on the schema's own parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Return the most recent U.S. CPSC product recalls.' It further clarifies scope by specifying date-sorted, newest-first ordering and the SaferProducts.gov source, which clearly differentiates it from the sibling cpsc_recall_search tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for its use case — retrieving recent recalls over a configurable lookback window — but it never explicitly compares this tool to cpsc_recall_search or states when one should be preferred over the other. The intended usage is implied by the word 'recent' and date-sorting, not directly articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.