Moltline Merchant Math

Processor Fees

processor_fees

Read-onlyIdempotent

Break down payment-processor fees: fee, net, and effective rate. FREE.

Uses editable presets for stripe/paypal/square/shopify (verify current rates) or your own custom_pct + custom_fixed. Typical input {"amount": 1000, "processor": "stripe", "transactions": 10} returns {"gross": 1000, "fee": 32.0, "net": 968.0, "effective_rate_pct": 3.2, "note": "..."}.

Use when the charge amount is known and the net payout is the question. Not for the reverse: the gross needed to net a target is charge_to_net. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "amount must be > 0, transactions >= 1"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`amount`	Yes	Total gross amount processed; must be greater than 0.
`processor`	No	One of "stripe", "paypal", "square", "shopify", or "custom". Default "stripe".	stripe
`custom_pct`	No	Percentage fee used when processor is "custom", e.g. 2.5 for 2.5%.
`custom_fixed`	No	Fixed per-transaction fee used when processor is "custom", e.g. 0.25.
`transactions`	No	How many transactions the amount is spread across; at least 1. Default 1.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior4/5

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

The description declares read-only and idempotent behavior, consistent with annotations, but adds value by detailing the error handling strategy: 'never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}.' This goes beyond what annotations provide, though annotations already cover the core safety profile.

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 concise and well-structured: a one-line summary, then presets, a typical example, usage guidance, and error behavior. Every sentence adds meaningful information without redundancy. It is front-loaded with the tool's core action.

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

Completeness5/5

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

Given the tool's calculation nature, the description covers the return value format (including a note field), error format, presets, and safe retry advice. The presence of an output schema further complements this, but the description itself is self-sufficient for ensuring correct invocation.

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

Parameters4/5

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

Schema description coverage is 100%, so parameters are already well-documented. The description enhances understanding by providing a concrete example ('{

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 the tool's purpose: 'Break down payment-processor fees: fee, net, and effective rate.' It explicitly differentiates from the sibling 'charge_to_net' by saying 'Not for the reverse: the gross needed to net a target is charge_to_net.' This provides specific verb+resource identification and distinguishes it from related tools.

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

Usage Guidelines5/5

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

The description provides explicit guidance: 'Use when the charge amount is known and the net payout is the question. Not for the reverse: the gross needed to net a target is charge_to_net.' It also mentions that errors are returned safely and retrying is always safe, further clarifying proper usage.

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

All Moltline Merchant Math tools Inspect

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TDQS

A4.6/5.0

Disambiguation4/5

Each tool targets a distinct calculation scenario (fees, discounts, installments, invoicing, proration). The descriptions explicitly state when to use each and when not to, reducing ambiguity. Minor overlap exists between processor_fees and charge_to_net, but the inverse relationship is clearly documented.

Naming Consistency5/5

All tool names use a consistent noun_verb pattern (e.g., charge_to_net, discount_stack, installment_plan). The naming is descriptive and follows a predictable structure, making it easy for an agent to infer tool purpose from the name alone.

Tool Count5/5

With 6 tools, the count is ideal for the domain of merchant math. Each tool covers a distinct and essential calculation type without redundancy, and the scope is neither too thin nor overly heavy.

Completeness4/5

The tools cover core merchant math operations: fee calculation, gross-up pricing, discount stacking, installment splitting, invoicing, and proration. A minor gap is the absence of a tool for compound interest or tax-only calculations, but those may be out of scope for merchant math. Overall, the surface is well-rounded for its intended domain.

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