Deep analytics for a specific brand: daily performance trends, customer velocity, event activity, and program health metrics. Use this to understand how a brand's reward program is performing.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | Time period for analytics. Defaults to "30d". | |
| 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 transparency burden. It discloses the types of metrics returned, but does not clarify whether the operation is read-only, describe the output format, or mention any side effects. For an analytics tool this is a partial disclosure.
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 long, front-loaded with the core purpose, and every clause adds relevant detail. No filler or redundant information.
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 no output schema and no annotations, the description should explain enough about what the agent will receive. It lists metric categories but does not describe the response shape, the meaning of 'daily performance trends' in terms of data, or how this tool relates to similar brand-level tools like brand_health. Gaps remain.
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 description coverage is 100%, so both brandId and period are documented. The description adds little beyond the schema—it implies the brand and time period but does not elaborate on parameter syntax or behavior. Baseline 3 is appropriate since the schema already provides the needed semantics.
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 identifies the tool as providing 'deep analytics for a specific brand' and lists specific metrics (daily performance trends, customer velocity, event activity, program health metrics). It distinguishes from general brand info tools, though it lacks an explicit verb like 'get' or 'analyze', making it slightly less directive.
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 includes a clear use case: 'Use this to understand how a brand's reward program is performing.' It does not explicitly mention alternatives or when not to use the tool, but the use case provides adequate context for a sibling-rich environment.
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.
