recent_recallsMost recent US consumer-product recalls (CPSC), newest first — the "what got recalled lately" feed across all product categories. Use for "latest product recalls", "recent safety recalls this month". For a specific product use search_recalls.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look back this many days (default 30, max 365). | |
| limit | No | Max recalls to return (1-50, default 15). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "days": 30
+ },
+ {
+ "days": 90,
+ "limit": 25
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context beyond annotations by specifying the CPSC source, 'newest first' ordering, and cross-category scope, which helps the agent understand expected results.
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 description is two sentences, front-loaded with the core purpose ('Most recent US consumer-product recalls') and immediately followed by usage alternatives. Every sentence earns its place with no redundancy.
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?
The tool is simple with 2 optional params, no output schema, and rich annotations. The description fully covers what the tool does, when to use it, and how to distinguish it from the sibling search_recalls, making it complete for an agent's decision-making.
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?
The input schema provides 100% coverage of both parameters (days and limit) with clear descriptions, so the description does not need to add parameter details. The examples in the schema reinforce usage, but the description itself adds marginal value beyond the schema, hence baseline 3.
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 clearly states the tool lists the most recent US consumer-product recalls (CPSC), newest first, across all product categories. It explicitly distinguishes itself from search_recalls for specific product lookups, using a specific verb+resource structure.
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 provides explicit usage guidance: 'Use for "latest product recalls", "recent safety recalls this month"' and directly names the alternative tool for specific products (search_recalls). This clearly communicates when to use this tool versus alternatives.
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.
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.
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.
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.
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.