[PLAY] What VIA Points buy: brand-published redemption tiers. Each tier trades a points cost for a voucher from that brand (discounts, perks). Optionally filter by brand_slug. Redeem with redeem_points.
| Name | Required | Description | Default |
|---|---|---|---|
| brand_slug | No | Optional brand slug to filter offers |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the behavioral burden. It explains that offers are brand-published and each tier trades points for a voucher, which is meaningful context, but it does not disclose return format, authentication needs, or whether this is a safe read operation.
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 compact and front-loaded with the key purpose in the first phrase. It uses four short sentences with no wasted words, though the '[PLAY]' prefix and colloquial phrasing could be tightened.
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?
Given the tool has only one optional parameter and no output schema, the description provides sufficient context: what the offers are, how they work, the filter option, and the redemption path. It could mention return shape or more detail about voucher mechanics, but overall it is complete enough for a straightforward read-only listing.
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 schema already documents brand_slug as 'Optional brand slug to filter offers' with 100% coverage. The description repeats this filter capability but adds no new semantic detail beyond the schema, so it meets the baseline without exceeding it.
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 identifies the tool as retrieving brand-published redemption tiers that trade VIA Points for vouchers. This distinguishes it from sibling tools like get_offers by specifying the redemption-tier resource and its points-for-voucher nature.
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 implies when to use the tool: when exploring what VIA Points can buy. It also points to the related action 'Redeem with redeem_points', providing a clear next-step alternative, though it does not explicitly state when not to use it.
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.