Browse a brand's available rewards — discounts, free products, exclusive access, experiences, and more. See what's redeemable and what each costs.
| Name | Required | Description | Default |
|---|---|---|---|
| brandId | Yes | The brand identifier. Use rsnc_agent_list_brands to discover brands by category. | |
| category | No | Optional category filter for perks. |
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 must carry the burden of behavioral disclosure. It reveals that the output includes redeemability and cost, but does not explicitly confirm read-only behavior, mention permissions, pagination, or side effects. The verb 'browse' suggests non-mutating, but this is implicit rather than explicit.
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 two sentences, front-loaded with the verb 'browse', and provides useful examples. Every word earns its place with no redundancy or fluff.
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 simple read-only tool with a complete input schema and no output schema, the description gives the essential information: what it does and what the user can learn (redeemability, cost). It lacks an explicit return format but is still adequate for a tool of this simplicity.
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% — both parameters have descriptions (brandId points to list_brands; category is an optional filter). The description adds no new parameter semantics beyond what the schema already provides, so the 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 clearly states the verb 'browse' and resource 'a brand's available rewards', with examples (discounts, free products, etc.) and what info is shown (redeemability, cost). It does not explicitly distinguish from the similar sibling 'rsnc_agent_browse_perks', which may be a generic perk browsing tool, so it loses the top score.
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 usage for exploring a brand's rewards but does not explicitly state when to use this tool over alternatives like rsnc_agent_browse_perks or claim_reward. A prerequisite hint (use rsnc_agent_list_brands) exists in the schema but not in the description, leaving the guidance implicit.
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.
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.
