Get real-time network analytics: total users, brands, rewards distributed, trending categories, and growth metrics across the entire Resonance network.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | Time period for analytics. Defaults to "30d". |
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 provided, the description carries full burden for behavioral disclosure. It implicitly conveys a read-only operation via 'Get' and notes 'real-time' plus network-wide scope, but it does not state explicit safety (e.g., read-only) or potential caveats like caching or authentication. This is adequate for a simple analytics fetch but not rich.
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 primary purpose and uses a colon to enumerate what the analytics include. Every word contributes value, 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 simple (one optional parameter, no output schema or annotations), and the description sufficiently conveys what data it returns. It could be more complete by referencing the period parameter directly or distinguishing from sibling analytics tools, but overall it provides enough context for an agent to invoke it correctly.
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% for the only parameter (period), including enum and default. The description adds no extra parameter semantics, so the baseline score of 3 applies; the schema already documents all needed details.
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 retrieves network analytics, specifying concrete metrics like total users, brands, rewards, and trending categories. It identifies the resource scope (entire Resonance network) but does not explicitly distinguish it from similar sibling tools such as network_trending or network_stats, thus missing a point for differentiation.
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 gives no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or which sibling tools are better suited for particular use cases, leaving the agent to infer its applicability from the general analytics context.
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.
