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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').

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / limit / maximum
      Added value: +200
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • changedInput schema / properties / limit / type
      Previous value: -"number"New value: +"integer"
  2. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld, so the safety profile is covered; the description adds genuinely new context by noting the API is 'keyless' (no auth) and enumerating the returned fields, which matters since there is no output schema. It stops short of noting rate limits or pagination behavior.

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?

Three sentences, front-loaded with purpose, then filters, then return shape. Minor redundancy in the trailing 'Data: CPSC/SaferProducts.gov' which repeats the source already named in sentence one.

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?

With 8 optional params and no output schema, the description usefully enumerates the return fields and notes keyless access, covering most of what an agent needs. It omits pagination behavior and the default limit, which the schema only partially implies.

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 the schema already documents all 8 filters with examples (e.g. 'fire', '26561', YYYY-MM-DD). The description restates the filter dimensions without adding syntax or format detail beyond the schema, which is the baseline-3 case.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Search) and resource (U.S. CPSC product recalls) plus the data source (SaferProducts.gov). However, it does not distinguish itself from the sibling cpsc_recent_recalls, leaving the agent to infer which recall tool to pick.

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

Usage Guidelines2/5

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

The description lists filterable dimensions but gives no when-to-use guidance, no prerequisites, and no mention of the sibling cpsc_recent_recalls or other recall tools (vehicle_recalls, recall_screen). Usage context is left entirely to 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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