Moltline Merchant Math

Charge To Net

charge_to_net

Read-onlyIdempotent

Compute the gross price to charge so you net a target after fees. FREE.

'Charge X to receive Y' after percentage + fixed processor fees. Typical input {"net_target": 100, "pct_fee": 2.9, "fixed_fee": 0.30} returns {"charge": 103.4, "fee": 3.4, "net": 100.0}.

The inverse of processor_fees - solves for gross from a target net. Use when the payout is the fixed requirement. Not when the price is already set. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "net_target > 0 and pct_fee < 100 required"}). 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
`pct_fee`	No	Processor percentage fee, below 100, e.g. 2.9 for 2.9%. Default 2.9.
`fixed_fee`	No	Processor fixed fee per charge. Default 0.30.
`net_target`	Yes	The amount you want to receive after fees; must be greater than 0.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A5/5.0

Behavior5/5

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

Annotations already provide readOnlyHint=true and idempotentHint=true. The description reinforces that 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry.' It also discloses error handling behavior (returns error object, never protocol error). No contradictions.

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: purpose statement, example, usage guidance, error behavior, and idempotency note. Every sentence earns its place; no redundancy.

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 presence of an output schema, comprehensive annotations, and full parameter coverage, the description fills all gaps. It explains the computation, error handling, and safety, making it fully actionable for an AI agent.

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

Parameters5/5

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

Schema coverage is 100%, so baseline is 3. However, the description significantly adds value with a concrete example showing how net_target, pct_fee, and fixed_fee interact, and implies the formula. It also reiterates constraints (pct_fee below 100) beyond schema.

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 computes the gross price to charge to net a target after fees. It distinguishes itself from the sibling 'processor_fees' by explicitly stating it is the inverse, and provides a concrete example to clarify the purpose.

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 gives explicit guidance: 'The inverse of processor_fees - solves for gross from a target net. Use when the payout is the fixed requirement. Not when the price is already set.' This directly tells the agent when and when not to use the tool, and names the alternative.

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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