Update default cashback_rate and/or points_per_dollar for a program (same as merchant dashboard sliders)
| Name | Required | Description | Default |
|---|---|---|---|
| cashback_rate | No | New default cashback % for earn (1–100). Omit to leave unchanged. | |
| token_address | Yes | Token contract address (0x...) | |
| points_per_dollar | No | New points per $1 (0–1000, exclusive 0). Omit to leave unchanged. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It discloses that the tool updates the default values, but it does not mention side effects, permission requirements, or whether existing transactions are affected. The dashboard slider analogy adds some context, but important behavioral traits remain unspecified.
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 one short sentence that conveys the purpose and a useful analogy. No fluff; every word earns its place.
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 relatively simple config-update tool with a complete schema and no output schema, the description provides enough to understand the operation. It lacks explicit usage exclusions and some behavioral details, which are minor gaps given the tool's simplicity.
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 input schema already describes all three parameters with high coverage (100%), so the description doesn't need to repeat them. The description mentions the two optional parameters by name but adds no new semantic detail beyond the schema's existing descriptions of 'default' values.
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 'Update default cashback_rate and/or points_per_dollar' with a specific verb and resource, and the parenthetical 'same as merchant dashboard sliders' adds user context. This distinguishes it from sibling tools like update_program_status and create_loyalty_program.
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 that this tool modifies program config defaults, and the 'same as merchant dashboard sliders' analogy helps understand the intended use case. However, it does not explicitly contrast with alternatives like update_program_status, so it lacks explicit when/when-not guidance.
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, such as mint_loyalty_tokens vs earn_points (both mint loyalty tokens with fee bundles), create_loyalty_program vs register_loyalty_program (deploy vs register existing token), and activate_loyalty_program vs update_program_status (both manage program status). Some pairs like check_voucher_status and list_gift_certificates also overlap on voucher/certificate tracking. However, descriptions are detailed enough to reduce ambiguity for careful agents.
Most tools follow a consistent verb_noun snake_case pattern (e.g., create_reward, list_loyalty_programs). Subtle deviations include earn_points vs mint_loyalty_tokens (different verbs for similar mint operations) and use_voucher vs redeem_reward (different verb styles for redemption). Overall, the naming is predictable and understandable.
With 39 tools, this server is bloated for a loyalty platform. The addition of Bazaar discovery/payment tools and report management expands the scope, but many tools overlap or cover minor variations (e.g., two workflow planners: generate_program_defaults and get_program_workflow_status). A leaner set of 20-25 tools would be more appropriate.
The tool surface covers the full loyalty program lifecycle: creation, activation, registration, token minting/transfer, rewards, gift certificates, vouchers, offers, customer export, analytics, and reports. Notable gaps include no CRUD for personalized offers (only create), no edit capability for rewards (only status changes), and no direct function to list all vouchers by merchant (only status check by code). These are minor workarounds.