Get intelligence on a brand's perks: heat scores, conversion funnels, supply depletion rates, and retention impact. Helps optimize perk catalogs.
| 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 of behavioral disclosure. It indicates the tool returns specific intelligence metrics but does not mention side effects, permissions, data freshness, or whether the operation is read-only (though 'Get' implies it). This is adequate but leaves room for more 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 two sentences, front-loaded with the main verb and resource, and lists useful details without fluff. The second sentence adds a clear purpose. It is appropriately sized with no unnecessary 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?
Given the tool's simplicity (one parameter, no output schema), the description provides a solid list of returned metrics, which is sufficient. It could benefit from noting the time period or aggregation level, but overall it is reasonably complete for an intelligence-gathering tool.
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 input schema has 100% coverage with brandId described as 'The brand identifier.' The description does not add any additional meaning to the parameter, so it scores at the baseline for full schema coverage.
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 intelligence on a brand's perks and lists specific metrics (heat scores, conversion funnels, supply depletion rates, retention impact), making the purpose clear. However, it does not explicitly distinguish itself from sibling tools like rsnc_agent_perk_analytics, so it earns a 4 rather than 5.
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 phrase 'Helps optimize perk catalogs' implies a use case, but there is no explicit guidance on when to use this tool versus alternatives such as rsnc_agent_perk_analytics or rsnc_agent_brand_analytics. The description provides some context but lacks clear exclusions or alternative recommendations.
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.
