Get details on a brand's reward program: what actions earn cashback (purchases, signups, reviews, referrals), how much you earn per action, and any bonus opportunities.
| Name | Required | Description | Default |
|---|---|---|---|
| brandId | Yes | The brand identifier. Use rsnc_agent_list_brands to discover brands by category. |
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?
With no annotations, the description carries the burden of behavioral disclosure. It accurately describes the kind of data returned (actions, rates, bonuses) but doesn't explicitly state that the operation is read-only, what happens if the brand is not found, or any limitations. The description is honest but lacks explicit safety or side-effect details.
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 core purpose and then enumerates specifics. It contains no redundant phrases, making it efficient and easy to parse.
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?
The tool is simple (one parameter, no output schema), and the description covers the main aspects of what the tool returns. It doesn't detail error scenarios or response format, but for a straightforward getter, the provided info is largely sufficient. A slightly longer mention of return structure would push it to a 5.
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 brandId described as 'The brand identifier. Use rsnc_agent_list_brands to discover brands by category.' The description adds context about what the tool returns but doesn't elaborate on the parameter itself beyond the schema, so the schema already does the heavy lifting. 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 the tool's function: 'Get details on a brand's reward program' and specifies exactly what those details include (earning actions, cashback amounts, bonuses). This distinguishes it from sibling tools like brand_analytics, brand_health, and brand_perks, which focus on different aspects.
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 the tool is for retrieving reward program details, but it doesn't explicitly compare it to alternatives or state when to prefer this over siblings like brand_perks or compare_brands. The only additional guidance is in the schema hint to use rsnc_agent_list_brands for discovering brands, which addresses how to find the brandId, not when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Several tools have overlapping purposes, e.g., rsnc_agent_best_deals, rsnc_agent_route_purchase, and rsnc_agent_compare_cashback all help find the best purchase/reward option. Similarly, rsnc_agent_network_info, rsnc_agent_network_stats, and rsnc_agent_network_analytics provide similar network overview data with unclear boundaries.
All tools share the consistent 'rsnc_agent_' prefix, but the remainder mixes verb-first patterns (browse_perks, claim_reward, create_event) with noun-first patterns (brand_analytics, network_flows, perk_intelligence). This inconsistency makes the tool surface less predictable than a uniform verb_noun scheme.
With 45 tools, the server feels over-scoped for a rewards network. While the domain is broad, many tools are highly granular analytics variations, and the count exceeds the 25+ threshold, adding cognitive load and diminishing coherence.
The tool set covers the core lifecycle: brand discovery, onboarding, event/perk creation and updates, reward processing, user balance/stats, and redemption. Minor gaps exist, such as no delete operations for events/perks and no direct user listing, but agents can work around these.
