See what's trending across the Resonance network: hottest perk categories, fastest growing brand categories, and overall network health metrics.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of trending items per category. Defaults to 10. | |
| period | No | Time period for trending data. Defaults to "7d". |
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?
There are no annotations, so the description carries the full burden of behavioral disclosure. The verb 'See' implies a read-only operation, but the description does not explicitly state that it has no side effects, does not modify data, or mention any auth or rate limiting considerations. It also does not provide any context about the response format or potential limitations of the trending data.
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 purpose ('See what's trending') and then lists the specific trending elements. Every word is informative and there is no fluff 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?
For a simple read-only tool with optional parameters and no output schema, the description gives a general sense of the return content (categories and health metrics) but lacks specifics about the data's structure or examples. Given the presence of many similar sibling tools, more context about the exact type of output would improve completeness, but it is minimally viable.
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 covers both parameters (limit and period) with clear descriptions, so the schema itself carries the semantics. The description adds no extra meaning beyond what the schema already provides. With 100% schema description coverage, the baseline score of 3 is appropriate.
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 what the tool does: it shows trending content across the Resonance network, including perk categories, brand categories, and network health metrics. The verb 'See' is somewhat passive, but the scope and content of results are specified, which distinguishes it from sibling tools like network_stats or network_analytics, though not explicitly.
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 provides no guidance on when to use this tool versus alternatives. There are many sibling tools (network_analytics, network_stats, brand_analytics, etc.) that could overlap, but the description does not mention when this trending tool is preferred or when to avoid it. No exclusions or alternatives are given.
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.
