Get the persona breakdown of users who claimed a specific perk: archetypes, engagement levels, and platform distribution.
| Name | Required | Description | Default |
|---|---|---|---|
| brandId | Yes | The brand identifier. | |
| collectionId | Yes | The perk collection ID. |
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 must carry the behavioral burden. It discloses that the tool is read-only ('Get') and specifies the output dimensions, but it omits access requirements, potential limitations, or response structure. For a simple read tool this is adequate but not comprehensive.
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 a single, well-structured sentence that front-loads the verb 'Get' and immediately states the object and specifics. Every word contributes to understanding, with no filler or redundancy.
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 a straightforward read operation with two well-documented parameters and no output schema. The description explains the purpose and the included dimensions, which is sufficient for an agent to select the tool confidently. A brief mention of output format or data freshness would elevate completeness, so it's a 4.
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 already provides clear descriptions for both parameters (brandId and collectionId), with 100% coverage. The tool description adds no additional parameter-level information, so it remains at the schema-driven 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 uses the specific verb 'Get' and identifies the precise resource: persona breakdown of users who claimed a specific perk. It further enumerates the output categories (archetypes, engagement levels, platform distribution), clearly distinguishing it from sibling tools like brand_audience or user_persona.
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 clearly implies the usage context: when an agent needs audience insights for a specific perk based on claimers. It does not explicitly mention alternative tools or exclusions, but the scope is well-defined and self-explanatory, so it earns a 4.
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.
