[BROWSE] List active voucher offers (perks) from brands. Vouchers are bonus perks bundled with purchases. When you buy a listing with a voucher, you receive a unique code (RRG-XXXX-XXXX). Use redeem_voucher to redeem it. Optionally filter by brand_slug.
| Name | Required | Description | Default |
|---|---|---|---|
| brand_slug | No | Optional brand slug to filter offers by a specific brand |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It says 'active' offers, implying a read-only operation, and provides useful domain context about voucher codes (RRG-XXXX-XXXX). But it does not mention pagination, rate limits, or explicit non-mutating guarantees. 'List' helps, but the description is not rich enough for a 4.
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 three sentences, front-loaded with the purpose, and carries no redundant phrasing. The [BROWSE] tag adds orientation. Every sentence contributes value.
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 list tool with one optional parameter and no output schema, the description fully explains what it returns (active voucher offers) and provides the redemption context. An agent can confidently decide when and how to invoke it.
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 description for brand_slug already covers the parameter purpose at 100% coverage. The tool description only restates 'Optionally filter by brand_slug' without adding syntax or format details. Baseline 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 opens with a clear verb+resource: 'List active voucher offers (perks) from brands.' It distinguishes itself from siblings like redeem_voucher by explicitly stating the redemption action elsewhere. The context about purchases and code formats clarifies the domain.
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 explicitly tells the agent to use redeem_voucher for redemption, naming a sibling tool as the next step. It also notes the optional brand_slug filter. However, it does not contrast with get_redemption_offers or list_brands, so the when-not guidance is incomplete.
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.