Get a unified view of a user's rewards across ALL brands — total balance, per-brand breakdown, active streaks, and the best perk they can afford right now. The cross-brand loyalty dashboard. Requires authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| userId | Yes | User identifier (email address or wallet address). |
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 provided, the description carries the full burden. It discloses the authentication requirement, implies a read-only operation through 'Get' and 'view,' and indicates real-time assessment via 'right now.' While it doesn't explicitly state 'no side effects,' the terminology strongly suggests a safe query, and the listed outputs add useful behavioral context.
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 concise and front-loaded with the core action ('Get a unified view...'), followed by concrete output elements and a brief authentication note. Every sentence adds value without fluff or redundancy.
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?
Despite having no output schema, the description clearly enumerates the key return components (total balance, per-brand breakdown, active streaks, best perk) and the required authentication. For a simple one-parameter query tool, this is complete and sufficient for an agent to invoke correctly.
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% for the single userId parameter, which is fully described as 'User identifier (email address or wallet address).' The description does not add any additional parameter information, but the schema already provides complete semantics, so the baseline of 3 applies.
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 gets a unified view of a user's rewards across all brands, listing specific outputs: total balance, per-brand breakdown, active streaks, and best perk. It distinguishes from siblings by emphasizing 'ALL brands' and the cross-brand dashboard nature, unlike user_balance or user_stats.
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 for when to use this tool: when a cross-brand unified overview is needed, as indicated by 'across ALL brands' and 'cross-brand loyalty dashboard.' It does not explicitly name alternatives or exclusion criteria, but the strong contrast with single-brand tools like user_balance makes the intended use clear.
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.
