rsnc_agent_network_info
Returns info about the Resonance rewards network — a cross-brand cashback and perks marketplace covering retail, dining, travel, gaming, and more.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Returns info about the Resonance rewards network — a cross-brand cashback and perks marketplace covering retail, dining, travel, gaming, and more.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 burden of behavioral disclosure. It only says 'Returns info,' which implies a read-only operation but does not disclose output format, data freshness, or any potential side effects. This adds minimal behavioral context beyond the verb itself.
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?
A single sentence that is front-loaded with the action ('Returns info') and the resource ('Resonance rewards network'). No unnecessary words or repetition; it is efficiently structured.
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 zero-param tool, the description gives useful context about the network's vertical coverage, but it does not clarify what specific 'info' will be returned or how it relates to sibling analytics tools. Since there is no output schema, the description is adequate but somewhat vague.
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 tool has zero parameters, making schema coverage trivially 100%. Per the baseline for 0-param tools, a score of 4 is appropriate since no parameter explanation is needed.
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 'Returns info about the Resonance rewards network' with a scope of 'cross-brand cashback and perks marketplace covering retail, dining, travel, gaming, and more.' However, it does not explicitly differentiate from sibling tools such as network_stats or network_analytics, which also target network-level information.
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?
No guidance is provided on when to choose this tool over sibling alternatives like network_stats, network_analytics, or network_trending. The description implies general use but does not state exclusions, prerequisites, or alternative tools.
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.