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Glama

LiveDataLink

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. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description only needs to add operational context. It does so by noting 'keyless' access, naming the data source, and listing the exact returned fields including the official recall URL. This goes beyond the 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 compact and front-loaded with the core action and source. The only minor inefficiency is that 'Data: CPSC/SaferProducts.gov' repeats the source already named in the first sentence, so it's not perfectly zero-waste, but it is still appropriately sized and structured.

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 no output schema, the description compensates by enumerating return fields, which is good. It covers nearly all parameter categories except pagination/limit behavior and date format examples, but the schema already documents date formats and the default limit. Overall this is sufficient for an 8-parameter read-only search 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 the baseline is 3. The description summarizes the filters that map to schema parameters but adds no new meaning beyond what each parameter description already states. It doesn't mention the 'limit' parameter explicitly, though the schema covers it.

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 specific verb ('Search'), names the exact resource (U.S. Consumer Product Safety Commission product recalls via SaferProducts.gov), and enumerates the available filters. It clearly distinguishes itself from related tools like cpsc_recent_recalls by emphasizing filtering and search rather than just listing recent recalls.

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 the tool ('Filter by...', 'Returns...') but never explicitly states when to prefer this over siblings like cpsc_recent_recalls or vehicle_recalls. It gives clear context but no exclusions or alternative routing guidance.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.