Check how much cashback a user has earned at a specific brand and how much they can spend on rewards. Requires authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| userId | Yes | User identifier (email address or wallet address). | |
| 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?
No annotations are provided, so the description carries the full burden. It discloses the authentication requirement and indicates the return values (earned cashback and spendable rewards), which implies a read-only operation. However, it does not explicitly state 'read-only' or mention side effects (e.g., whether it updates anything), nor does it provide error-handling details. This is adequate but not rich.
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 only two sentences, with the primary action front-loaded and no redundant information. Every word contributes to understanding the tool's purpose, scope, and authentication requirement.
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's simplicity (2 required params, no output schema) and the fact that the description already explains the return values, it is mostly complete. However, it lacks any mention of error conditions, default behavior, or limitations (e.g., what if the user has no cashback), which would be useful in edge cases but are not critical for a basic balance check.
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 description coverage is 100%: both userId and brandId already have clear descriptions. The tool description adds no additional parameter-level meaning beyond what the schema provides, so the baseline score 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 uses the specific verb 'Check' and clearly identifies the resource: a user's cashback earned and spendable reward amount at a specific brand. This scope ('at a specific brand') distinguishes it from more general user-level tools like rsnc_agent_user_stats or rsnc_agent_my_rewards.
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 provides clear context: it checks per-brand balance and requires authentication. However, it does not explicitly state when to prefer this tool over alternatives (e.g., compare_cashback or my_rewards) or when not to use it, so it earns a 4 rather than a 5.
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.
