Manage API keys for brands you onboarded. Rotate, revoke, or check status. Never exposes secrets — credentials are accessed through the partner portal.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Key management action. | |
| brandId | Yes | Brand wallet address. |
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. It adds valuable behavioral disclosure: 'Never exposes secrets — credentials are accessed through the partner portal.' However, it does not mention side effects of rotate or revoke, permission requirements, or reversibility. The single behavioral note is useful but incomplete.
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 short sentences, front-loaded with the main purpose, and every phrase contributes meaning. It avoids empty filler and is highly scannable.
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 tool with only 2 parameters, no output schema, and no annotations, the description gives essential information: operations, scope, and a security behavior. However, it does not describe the return format or the outcome of each action, which an agent would need to know for status checks or confirming success. It is adequate but not complete.
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 coverage is 100%, so the schema already documents both parameters. The description adds a bit of meaning by specifying the action enum values in prose and the brand scope ('brands you onboarded'), but it does not explain the action semantics beyond what the enum labels already imply. The description adds marginal value over the schema.
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: 'Manage API keys for brands you onboarded.' It specifies the exact operations (rotate, revoke, or check status) and distinguishes itself from sibling tools by focusing on key management. The verb 'manage' is made specific by the listed actions.
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 context by scoping to 'brands you onboarded' and hints the tool never exposes secrets, implying use when key operations are needed. However, it does not explicitly state when to choose this vs. alternatives, nor does it clarify when to use rotate vs. revoke vs. status. No exclusions are provided.
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.
