Claim a reward for a valuable agent action. Rewards are paid from the Resonance network fund with daily and weekly rate caps. Discovery earns 100 RSNC, onboarding earns 500 RSNC.
| Name | Required | Description | Default |
|---|---|---|---|
| rewardType | Yes | Type of action to claim reward for. | |
| brandAddress | Yes | The brand address associated with this action. |
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 burden. It discloses that rewards are paid from the Resonance network fund and subject to daily and weekly rate caps, which is important behavioral context. It also specifies exact reward amounts per action type. However, it does not mention potential failure cases, idempotency, or prerequisites beyond the schema parameters.
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 three sentences with no filler. The lead sentence states the purpose immediately, followed by key facts about funding and reward amounts. This is concise and well-structured.
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 (2 required params, no output schema). The description covers the core behavior, reward amounts, and rate caps, making it usable without additional explanation. The lack of output schema is mitigated by the straightforward nature of a claim action, though details about return values are not specified.
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?
The schema already covers both parameters with 100% description coverage, so baseline is 3. The description adds meaningful context by mapping rewardType values to specific amounts ('Discovery earns 100 RSNC, onboarding earns 500 RSNC'), going beyond the enum labels. It does not add additional meaning for brandAddress beyond what the schema states, but the overall value justifies a 4.
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: 'Claim a reward for a valuable agent action.' It specifies reward types and amounts ('Discovery earns 100 RSNC, onboarding earns 500 RSNC'), distinguishing it from read-only sibling tools like rsnc_agent_my_rewards and action tools like rsnc_agent_redeem_perk. This makes it easy for an agent to identify this as the reward-claiming tool.
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 correct usage: use this tool after performing a discovery or onboarding action to claim a RSNC reward. It provides context about reward amounts and rate caps but does not explicitly mention alternatives or exclusions (e.g., when to use my_rewards instead). Thus it gives clear usage context without explicit when-not-to-use guidance.
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.
