[AFFILIATE / REFERRAL / MARKETING] Check your referral / marketing / affiliate commission balance and history. Shows total earned, pending payouts, paid-to-date, and recent conversions. Identified by wallet. Works for humans and AI agents alike.
| Name | Required | Description | Default |
|---|---|---|---|
| wallet_address | Yes | Your marketing agent wallet address |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses behavior by stating 'Identified by wallet' and enumerating what is shown, implying a read-only operation. However, it does not address potential errors, permissions, or rate limits.
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 four short sentences, front-loaded with purpose, then details, identification method, and audience. Every sentence provides useful information with no filler.
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 one-parameter read tool with no output schema, the description covers purpose, returned data, and identification. It is sufficiently complete for most use cases, though explicit output format or error handling is not described.
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% with a description already explaining 'wallet_address'. The tool description adds 'Identified by wallet', reinforcing the parameter's role, but this is marginal beyond 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 specifies a clear verb ('Check') and resource ('referral / marketing / affiliate commission balance and history'), and details the outputs (total earned, pending payouts, paid-to-date, recent conversions). This distinguishes it from siblings like get_points_balance and get_offers.
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 clear context for use (commission balance/history inquiries) and explicitly states it works for both humans and AI agents. It does not mention alternatives or exclusions, but the use case is unambiguous.
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.
Most tools have distinct purposes with clear category tags. Minor potential confusion between get_current_brief vs list_briefs and get_brand vs get_brand_mcp_endpoint, but descriptions clarify intent.
All tools follow a consistent verb_noun pattern using lowercase with underscores, e.g., check_agent_standing, list_drops, submit_design. No mixing of conventions.
32 tools is on the high side, but the server covers a wide domain including browsing, purchasing, design submission, concierge, and marketing. Each tool serves a specific function, though some consolidation could be possible.
The tool surface covers the full lifecycle of browsing, purchasing, design creation, commissions, and concierge services. Minor gaps like refund handling are absent, but the core workflows are well-supported.