Compare 2-5 brands side-by-side across all performance metrics: velocity, customers reached, transactions, LTZ distributed, event diversity, and perk utilization. Returns a structured comparison matrix with per-metric winners and insights.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | Time period for comparison. Defaults to "30d". | |
| brandIds | Yes | Array of 2-5 brand identifiers to compare. |
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?
With no annotations, the description carries the transparency burden. It explicitly states the return format ('structured comparison matrix with per-metric winners and insights'), which is useful. However, it does not disclose potential limitations like data freshness, permissions, or that it is a read-only operation, though 'Compare' implies safety.
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?
Two sentences: the first states purpose and scope, the second describes the output. No unnecessary words, front-loaded with the primary action.
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 only 2 parameters (fully described in schema), no output schema, and moderate complexity. The description adequately covers output structure and metrics, making it sufficient for an agent to select and invoke the tool. Missing minor details like period default, but that is in the schema.
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%, so baseline is 3. The description adds meaning by naming the specific metrics compared (velocity, customers reached, transactions, etc.), which goes beyond the schema's generic parameter descriptions and helps the agent understand the tool's scope.
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 a specific verb ('Compare') with a clear resource ('brands'), specifies the count range (2-5), and enumerates the performance metrics covered. This clearly distinguishes it from sibling tools like compare_cashback, which is narrower in scope.
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 usage by listing comprehensive brand performance metrics, making it clear when to use this instead of a single-brand analytics tool or a cashback-only comparison. However, it does not explicitly name alternatives or state when not to use it.
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.
