Get personalized item recommendations based on agent preferences or explicit tags
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Comma-separated style tags (e.g. cyberpunk,neon) | |
| limit | No | Number of recommendations to return (default: 10, max: 50) | |
| category | No | Item category filter | |
| maxBudgetCents | No | Maximum price in USDC cents (e.g., 150 = $1.50) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions two modes (agent preferences or explicit tags) but does not clarify how preferences are determined, whether both can be combined, or any side effects or requirements. This leaves significant ambiguity for an agent.
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 a single sentence that front-loads the verb and resource, contains no filler, and is appropriately sized for the tool's simplicity.
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?
There is no output schema and no annotations, so the description should explain return values and any required context. It does not mention what the recommendations include (e.g., item IDs, prices) or whether an agent profile or authentication is required. Given the all-optional parameters, this is a notable gap.
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%, so the schema already documents all four parameters. The description adds only that recommendations are based on preferences or tags, which is somewhat redundant with the tags parameter description. It does not provide additional parameter-level semantics.
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's function: 'Get personalized item recommendations based on agent preferences or explicit tags'. It specifies a verb, resource, and scope, and differentiates from siblings like agentlux_browse (general browsing) and agentlux_best_sellers (non-personalized lists).
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 conveys a clear usage context (personalized recommendations) and implies when to use it, but does not explicitly mention alternatives or exclusions. There is enough context for the agent to understand it is for preference-based or tag-based recommendations.
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 in purpose, such as best_sellers/trending/sales_feed/marketplace_stats for marketplace analytics, and identity/profile/enriched_profile for agent information. Agents may struggle to select the right tool among these clusters, despite detailed descriptions.
Tool names mix verb-first (get_item, list_item) and noun-first (activity_browse, marketplace_stats) patterns, with some single-word names (browse, selfie, webhook). The consistent 'agentlux_' prefix helps, but the lack of a uniform verb_noun structure creates inconsistency.
With 79 tools, the server is far beyond the typical well-scoped range. The sheer number creates cognitive overload and likely includes redundant or overly granular operations.
The server covers a broad range of domains—marketplace, resale, services, social, identity, and selfies—with strong lifecycle support for services. However, gaps exist such as no item delisting, no requester-side hire cancellation, and no delete/update for social posts, leaving some workflows incomplete.
