Check your agent's RSNC reward earnings, activity stats, and remaining rate cap allowances. Shows how much the agent has earned for discovering brands, onboarding partners, and driving network growth.
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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 the full burden of behavioral disclosure. It uses 'Check' and 'Shows,' implying a read-only operation with no side effects. However, it does not explicitly state read-only status, mention authentication requirements, or clarify whether 'remaining rate cap allowances' refers to the tool's own rate limits or the agent's reward caps. This leaves some ambiguity, though the overall intent is clear.
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 concise at two sentences, with the key purpose front-loaded in the first sentence. Every phrase contributes value: it names the specific data areas (earnings, activity stats, rate cap allowances) and then elaborates on reward sources. There is no redundant or filler content.
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 zero-parameter tool with no output schema, the description is nearly complete. It explains what the tool checks and what information it surfaces, including the types of earnings. It stops short of specifying the exact return format or data structure, but given the low complexity and lack of parameters, the description provides adequate context for an AI agent to understand the tool's function.
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 takes zero parameters, and the schema description coverage is effectively 100% (vacuously). The baseline for no parameters is 4, and the description does not need to explain parameter semantics. It simply describes what the tool checks, which is sufficient.
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's purpose with a specific verb and resource: 'Check your agent's RSNC reward earnings, activity stats, and remaining rate cap allowances.' It distinguishes itself from siblings like rsnc_agent_claim_reward and rsnc_agent_user_balance by focusing on agent-specific reward earnings and rate caps, making its role unambiguous.
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 clear context for when to use the tool: whenever the agent needs to check reward earnings, activity stats, or rate cap allowances. It does not explicitly exclude alternatives or mention sibling tools, but the context is specific enough that an AI agent would understand when to invoke it, though it could benefit from a note on how it differs from similar reward-related 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.
