[BROWSE] Get full details for a specific listing by tokenId. Call this after list_drops to see what you are buying. Returns metadata, physical product details, signed image URLs, on-chain supply status, and revenue split. Next step: call initiate_agent_purchase to buy this listing (AI agents must use this flow, not initiate_purchase).
| Name | Required | Description | Default |
|---|---|---|---|
| tokenId | Yes | Token ID of the listing |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It does so by enumerating return contents: 'metadata, physical product details, signed image URLs, on-chain supply status, and revenue split.' It clearly implies a read-only operation without side effects. Minor omissions like auth/error behavior are acceptable for such a simple read tool.
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?
Three tightly packed sentences: purpose, return contents, and next-step guidance. No filler or redundancy, with the most critical information front-loaded.
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 one-parameter read tool without an output schema, the description is complete. It covers purpose, input context, return values, and workflow integration, leaving no important gaps.
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 coverage is 100% with tokenId described as 'Token ID of the listing'. The description adds the contextual detail that it is called after list_drops, but this does not add new parameter semantics beyond the schema. Baseline 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 'Get full details for a specific listing by tokenId' with a specific verb and resource. It distinguishes from sibling tools by positioning itself as the follow-up to list_drops, making its unique role explicit.
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?
Provides explicit when-to-use guidance: 'Call this after list_drops to see what you are buying' and 'Next step: call initiate_agent_purchase to buy this listing (AI agents must use this flow, not initiate_purchase).' This directly addresses usage context and alternatives, leaving no ambiguity.
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 have distinct purposes with clear category tags. Minor potential confusion between get_current_brief vs list_briefs and get_brand vs get_brand_mcp_endpoint, but descriptions clarify intent.
All tools follow a consistent verb_noun pattern using lowercase with underscores, e.g., check_agent_standing, list_drops, submit_design. No mixing of conventions.
32 tools is on the high side, but the server covers a wide domain including browsing, purchasing, design submission, concierge, and marketing. Each tool serves a specific function, though some consolidation could be possible.
The tool surface covers the full lifecycle of browsing, purchasing, design creation, commissions, and concierge services. Minor gaps like refund handling are absent, but the core workflows are well-supported.