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earn_points

Calculate and mint loyalty tokens based on purchase amount and program's cashback rate. Returns a fee-first calls[] bundle (protocol fee mint first, then the customer mint) — submit in order, atomically via EIP-5792 if supported, then call confirm_mint_fee (or POST /agent-api/mint/confirm). Unconfirmed fee obligations block future mints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cashback_rateNoOverride cashback rate (%). If omitted, uses the program's default rate.
token_addressYesToken contract address (0x...)
purchase_amountYesPurchase amount in currency units (e.g. dollars)
customer_addressYesCustomer wallet (0x...)

TDQS

A4.2/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully carries the transparency burden. It discloses the fee-first call ordering, atomic submission via EIP-5792, the required follow-up confirm_mint_fee call, and the consequence that unconfirmed fee obligations block future mints. These are substantial behavioral traits that an agent needs to know.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, dense with necessary information. It front-loads the primary action, then explains the output format and required follow-up. No redundant or filler content — every clause serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the essential workflow: input values, output bundle structure, submission order, atomicity requirement, confirmation step, and blocking behavior. It lacks minor contextual details like fee calculation basis or program activation prerequisites, but given the absence of output schema and annotations, it is highly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides descriptions for all parameters (100% coverage). The description mentions 'purchase amount' and 'cashback rate' but does not add new semantic detail about parameter formats or constraints beyond what the schema states. It meets the baseline for fully covered schemas.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Calculate and mint loyalty tokens based on purchase amount and program's cashback rate' — a specific verb and resource. It further distinguishes itself from the similar sibling tool 'mint_loyalty_tokens' by detailing the fee-first calls bundle, making the tool's unique behavior clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear step-by-step instructions (submit bundle, then call confirm_mint_fee) but does not explicitly contrast this tool with alternatives like mint_loyalty_tokens. It implies usage context through the workflow description but lacks explicit 'when to use vs. when not to use' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.