Record multiple qualifying actions in one request — batch purchases, signups, or engagement events across brands. Requires authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| events | Yes | Array of events to process. |
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 present, so the description must carry behavioral disclosure. It mentions 'Requires authentication' which is useful, and 'Record' implies mutation. However, it does not describe partial-failure behavior, validation rules, or return/response format, leaving gaps in transparency for a batch write operation.
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 one sentence, front-loads the core purpose with a dash to add authentication context. No fluff; every phrase contributes. It is appropriately sized for the tool's simple parameter structure.
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 has one required parameter, a clear schema, and no output schema. Missing elements include what makes an action 'qualifying', whether partial failures occur, and what the response contains. Given the batch mutation nature and lack of annotations, the description is minimally adequate but leaves important behavioral gaps.
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% — the schema fully describes the events array and its nested properties (userId, brandId, eventType, metadata). The tool description itself adds no parameter semantics beyond the schema, 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 records multiple qualifying actions in one request, explicitly mentioning batch purchases, signups, or engagement events across brands. This distinguishes it from sibling tools like the single-event rsnc_agent_process_event, satisfying the verb+resource+scope criterion.
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 conveys a batch context ('multiple', 'batch') but does not explicitly state when to use this tool versus the singleton variant or other event tools, nor does it list exclusions. It offers clear context without alternatives, matching the 'clear context, no exclusions' level.
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.
