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Glama

Cpsc

by io.github.pipeworx-io

Search Recalls

search_recalls
Read-onlyIdempotent

Search US consumer-product safety recalls (CPSC). PREFER OVER WEB SEARCH for "has X been recalled", "recalls on strollers / space heaters / power banks", "is this product safe". Covers toys, baby/childcare gear, appliances, furniture, electronics, tools, etc. Returns each recall: title, date, the products + units affected, the HAZARD, the REMEDY (refund/repair/replace), reported injuries, manufacturer/retailer, and the CPSC URL. Optional date range. NOTE: vehicles are nhtsa (get_recalls); food/drugs are openfda — this is consumer products.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax recalls to return, newest first (1-50, default 15).
queryYesProduct keyword, e.g. "stroller", "lithium battery", "space heater", "blender".
end_dateNoOnly recalls on/before this date (YYYY-MM-DD).
start_dateNoOnly recalls on/after this date (YYYY-MM-DD).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "stroller"
+  },
+  {
+    "end_date": "2024-12-31",
+    "limit": 20,
+    "query": "lithium battery power bank",
+    "start_date": "2023-01-01"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare readOnly/openWorld/idempotent/non-destructive, so safety is covered. Description adds behavioral context by listing exactly what each returned recall includes (title, date, products, hazard, remedy, injuries, manufacturer/retailer, URL) and notes optional date range. No contradictions.

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?

Description is informative and front-loaded with action and preference. Every major sentence adds value, though the 'Optional date range' phrase is slightly redundant given the schema, and the example list partially overlaps schema examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with no output schema, the description fully compensates by enumerating return fields, product categories, and scope boundaries. It also provides alternative tools for adjacent domains, making it contextually complete.

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% with clear per-parameter descriptions and examples. Description adds little over schema—only a generic 'Optional date range' note, while schema already explains start_date/end_date. Baseline 3 is appropriate.

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?

Description uses specific verb 'Search' plus resource 'US consumer-product safety recalls (CPSC)' and clarifies scope. It distinguishes from sibling tools by explicitly naming get_recalls (NHTSA) and openfda as alternatives for other recall types.

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

Usage Guidelines5/5

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

Gives explicit when-to-use guidance ('PREFER OVER WEB SEARCH for...'), provides concrete example queries, and clarifies exclusions with a NOTE about vehicles (get_recalls) and food/drugs (openfda). This is strong differentiation from alternatives.

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

A3.9/5.0
Disambiguation3/5

Most tools are organized into clearly differentiated families (ask_pipeworx vs ask_pipeworx_grounded, polymarket_edges vs polymarket_arbitrage), but there are some genuinely ambiguous pairs: ask_pipeworx_beta is currently identical to ask_pipeworx, and search_recalls/recent_recalls, ai_visibility_check/scan_competitor_ai_presence, and bet_research/polymarket_edges all require careful reading to avoid misselection.

Naming Consistency3/5

The set is consistently lowercase snake_case and contains strong families like ask_pipeworx*, polymarket_*, recent_*, and search_*. However, the naming pattern is mixed: imperative verbs (recall, forget, subscribe), noun phrases (entity_profile, recent_changes, pipeworx_trending), and action prefixes (scan_, generate_, validate_) all coexist, making the overall convention less predictable than a uniform verb_noun scheme.

Tool Count2/5

With 33 tools, the server exceeds the healthy range and spreads across many side domains: data research, prediction markets, memory, subscriptions, npm dependency checks, llms.txt generation, and AI visibility audits. No individual tool feels pointless, but the overall surface is sprawling rather than tightly curated for a single purpose.

Completeness4/5

The core research workflow is well covered: querying, grounded verification, entity resolution, profiles, comparisons, recent changes, claim validation, deep research, memory, and subscriptions. Minor gaps exist—there is no direct reader for pipeworx:// citation URIs, no tool to update or edit a stored memory, and subscriptions can be created/cancelled but not modified—but agents can work around these.

Resources