Get AI-recommended perk configurations for a brand. Suggests perk types, pricing, and supply limits based on audience composition and balance distribution patterns.
| Name | Required | Description | Default |
|---|---|---|---|
| brandId | Yes | The brand identifier. |
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 that suggestions are AI-generated and based on audience and balance patterns, but it does not specify read-only behavior, output format, potential non-determinism, or any side effects. This is a significant gap for an AI recommendation tool.
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 consists of two sentences: the first front-loads the action and resource, and the second adds relevant detail without repetition. Every word earns its place.
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?
With a single parameter and no output schema, the description explains the instrument's purpose and input basis but does not describe the return value or result format. It is understandable but leaves gaps about what 'perk configurations' look like, which is line with a moderate completeness score.
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 only parameter, brandId, is fully described in the schema with 'The brand identifier,' so schema coverage is 100%. The description adds implicit context by linking brandId to audience composition and balance distribution, but does not add syntax or format details beyond the schema, aligning with the baseline of 3.
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 purpose with a specific verb ('Get') and resource ('AI-recommended perk configurations' for a brand). It also details what is suggested (perk types, pricing, supply limits) and the basis (audience composition, balance distribution patterns), distinguishing it from sibling tools like rsnc_agent_brand_perks or rsnc_agent_perk_analytics.
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 needing AI-recommended perk configurations) and provides context about the data it uses, but it does not explicitly mention alternatives or when to choose this over related tools like rsnc_agent_brand_perks or rsnc_agent_suggest_events.
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.
