Claim a reward for a user — redeem a discount, freebie, or exclusive offer using their earned cashback. Returns a confirmation code. Requires authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| perkId | Yes | The ID of the perk to redeem. | |
| userId | Yes | User identifier (email 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?
With no annotations, the description must fully disclose behavior. It states that authentication is required and that a confirmation code is returned, but it does not explain side effects (e.g., cashback deduction, perk inventory changes), reversibility, or failure modes. For a mutation tool, this is insufficient transparency.
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 (three short sentences), front-loaded with the main action, and every sentence adds useful information: what it does, what it returns, and a prerequisite. No wasted words.
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 mutation tool with 3 parameters and no output schema, the description covers the core elements: purpose, return value, and authentication. It does not explain broader context like error scenarios or what happens to the user's cashback, but the tool's simplicity and schema completeness mitigate this.
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%, so the parameters are already well-documented. The description adds no additional parameter-specific details beyond the schema. 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 action: 'Claim a reward for a user — redeem a discount, freebie, or exclusive offer using their earned cashback.' It specifies the resource (perk/reward) and the context (using cashback). However, it does not distinguish from the sibling tool rsnc_agent_claim_reward, which likely overlaps in functionality.
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 when to use the tool (when a user wants to redeem a perk with cashback) and mentions authentication as a prerequisite, but it provides no explicit guidance on when not to use it or which alternative tools to consider. The existence of similar siblings like 'claim_reward' makes this gap notable.
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.
