Get platform-wide marketplace analytics including volume, listings, and average prices broken down by category
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | Time window for analytics (default: 30d) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the transparency burden. It discloses that the operation is read-only ('Get') and that it covers platform-wide data, but it does not mention any rate limits, data freshness, or response format. For a simple analytics query, this is acceptable but not exhaustive.
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, well-structured sentence that front-loads the action and resource, then lists the returned metrics. It contains no filler or redundant information, making it highly efficient.
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?
For a tool with one optional parameter and no output schema, the description adequately covers the core purpose and output. It specifies what is returned (volume, listings, average prices) and the scope (platform-wide, by category). It does not describe the response format or default period, but the schema covers the period default, so the missing response format is minor. Overall, the description is nearly complete.
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 has 100% coverage for the sole parameter 'period', including a description ('Time window for analytics (default: 30d)') and accepted enum values. The tool description adds no additional parameter semantics, but since the schema is fully descriptive, the baseline score of 3 is appropriate.
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 verb 'Get' and the resource 'platform-wide marketplace analytics', and specifies the exact metrics (volume, listings, average prices) and the breakdown by category. This distinguishes it from sibling tools like best_sellers or trending by focusing on broad analytics rather than specific item rankings.
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 gives clear context for when to use this tool: when you need platform-wide analytics. It implies this is the go-to for global marketplace statistics, but does not explicitly mention alternatives or exclusions. The phrase 'platform-wide' helps differentiate it from user-specific or item-specific tools.
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.
