Superseded listing, not a dead server. This entry was imported from an early Glama registration under com.moltlinestudio.mcp; the same server is listed under its official MCP Registry name — use com.moltlinestudio/merchant. Endpoint unchanged: https://mcp.moltlinestudio.com/merchant — still live, still free on the free tier. Only this duplicate entry is deprecated.
Payments and pricing math sellers do weekly, computed deterministically over MCP: processor_fees breaks down payment costs with editable presets, charge_to_net answers what to charge to net a target amount, invoice_total validates and totals line items, with discount_stack, proration, and installment_plan in the premium tier.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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.
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.
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.
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.
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.
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.
Stack discounts correctly: sequential vs additive comparison. PREMIUM (license).
The two methods differ — this shows both, the final price, and the true total discount. Typical input {"price": 200, "discounts_pct": [20, 10]} returns {"original": 200, "sequential_price": 144.0, "sequential_total_discount_pct": 28.0, "additive_price": 140.0, "difference": 4.0, "note": "..."}.
Use when more than one discount applies and the order changes the answer. Not for a single discount inside an invoice (invoice_total). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "price > 0 and at least one discount required"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| price | Yes | Original price before discounts; must be greater than 0. | |
| discounts_pct | Yes | Discount percentages in the order applied, at least one, e.g. [20, 10] for 20% then 10%. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds error-handling behavior (returns error object, never protocol error) and explicitly states read-only/idempotent retry safety, enhancing transparency beyond the schema.
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 multi-sentence but well-structured: purpose, example, usage guidance, error behavior, and safety note all contribute. It is not bloated; each sentence serves a distinct function.
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?
The tool has a simple schema, strong annotations, and an output schema, yet the description still explains return values, error cases, and usage boundaries, making it self-contained. No critical information is missing for an agent to select and invoke it correctly.
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 covers 100% of parameters, so baseline is 3. The description adds a concrete example mapping inputs to outputs and clarifies ordering semantics for discounts_pct, which strengthens understanding beyond the raw 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 uses specific verbs ('stack discounts', 'sequential vs additive comparison') and clearly states the tool's scope: computing both methods, final price, and true total discount. It also differentiates from sibling invoice_total by explicitly excluding single-discount invoice scenarios.
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?
Explicitly states when to use: 'Use when more than one discount applies and the order changes the answer.' Includes an exclusion: 'Not for a single discount inside an invoice (invoice_total),' naming the alternative. Also flags PREMIUM license.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Split a total into an installment schedule with optional deposit. PREMIUM (license).
Rounding remainders land on the final payment so the schedule always sums exactly. Typical input {"total": 1000, "installments": 3, "deposit_pct": 10} returns {"deposit": 100.0, "payments": [300.0, 300.0, 300.0], "check_sum": 1000.0}.
Use when one total is paid across scheduled dates. Not for partial-period adjustments (proration). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "total > 0 and 1-36 installments"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| total | Yes | Full amount to split; must be greater than 0. | |
| deposit_pct | No | Optional upfront deposit as a percentage of total, e.g. 10 for 10%. Default 0. | |
| installments | Yes | Number of payments after the deposit; 1 to 36. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses multiple behaviors beyond annotations: rounding remainder to final payment, error handling ('never raises a protocol error — it returns {"error": ...}'), and the consequence of read-only and idempotent semantics ('so after correcting the input it is always safe to retry'). Annotations already declare readOnlyHint and idempotentHint, but the description adds practical retry guidance. No contradictions with annotations.
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 well-structured with a clear logical flow: purpose, rounding behavior, example, usage guidelines, error handling, and idempotent note. Every sentence adds meaningful information. While it is not excessively verbose, it could be slightly more concise (e.g., the 'PREMIUM (license)' note might be merged). Overall, it is efficient and front-loaded.
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?
Given the tool has 3 parameters (100% schema coverage), an output schema, and clear annotations, the description covers all critical aspects: purpose, behavior, usage boundaries, error handling, and retry safety. The example effectively demonstrates the output shape. No significant gaps are present, making the description complete for agent selection and invocation.
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 clear descriptions for each parameter. The description adds value by providing a concrete example ('Typical input {"total": 1000, "installments": 3, "deposit_pct": 10} returns ...') that illustrates how parameters map to input and output. It also explains the rounding behavior affecting the total. This goes beyond the schema, earning a score above baseline 3.
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 the tool's purpose: 'Split a total into an installment schedule with optional deposit.' It uses a specific verb ('Split') and resource ('total into an installment schedule'), and directly contrasts with the sibling 'proration' by stating 'Not for partial-period adjustments (proration).' This effectively distinguishes the tool from at least one sibling, making purpose differentiation clear.
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 explicit when-to-use guidance: 'Use when one total is paid across scheduled dates.' It also gives a clear when-not-to-use scenario: 'Not for partial-period adjustments (proration),' which directly references a sibling tool as an alternative. Additionally, the note 'PREMIUM (license)' hints at licensing constraints, adding further context for agent decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Total an invoice: per-line totals, subtotal, discount, tax, grand total. FREE.
Typical input {"line_items": [{"desc": "Design", "qty": 2, "unit_price": 50}], "tax_pct": 8.5, "discount_pct": 10} returns {"lines": [{"desc": "Design", "qty": 2, "unit_price": 50, "line_total": 100.0}], "subtotal": 100.0, "discount": 10.0, "tax": 7.65, "total": 97.65}.
Use when several line items roll up with discount and tax. Not for one transaction's fees (processor_fees) and not for spreading a total over time (installment_plan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "line_items must contain at least one item,"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| tax_pct | No | Tax percentage applied after the discount, e.g. 8.5. Default 0. | |
| line_items | Yes | At least one line item object {"desc": str, "qty": number, "unit_price": number}; the first 100 items are used. | |
| discount_pct | No | Discount percentage applied to the subtotal. Default 0. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by confirming 'Every call is read-only and idempotent' and detailing error handling: 'this tool never raises a protocol error — it returns {"error": "..."}' plus a safe-to-retry guarantee. This goes beyond the annotations, but stops short of explaining rate limits or other potential constraints.
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 front-loaded with the core purpose and includes a compact JSON example. All sentences add value. It could be slightly more concise by omitting the repeated 'FREE.' tagline, but overall it is well-structured and efficient.
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?
The description covers purpose, usage guidelines, parameter behavior via example, error handling, safety guarantees, and clearly distinguishes from siblings. With annotations covering safety, an output schema present, and 100% schema coverage, the description is fully complete for the complexity of this tool.
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 description coverage is 100%, so the input schema already documents all three parameters. The description provides a typical usage example showing how parameters map to output, which adds practical context but does not add new semantic meaning beyond what the schema provides. Baseline 3 is appropriate.
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 'Total an invoice: per-line totals, subtotal, discount, tax, grand total.' It specifies the verb (total) and the resource (invoice), and distinguishes it from sibling tools like processor_fees and installment_plan by explicitly mentioning what it is not for.
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 explicitly says 'Use when several line items roll up with discount and tax. Not for one transaction's fees (processor_fees) and not for spreading a total over time (installment_plan).' This provides clear when-to-use and when-not-to-use guidance, naming specific sibling alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| 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. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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.
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.
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.
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.
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.
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.
Compute a prorated charge or refund for a partial billing period. PREMIUM (license).
Typical input {"amount_per_period": 90, "days_used": 10, "days_in_period": 30} returns {"days_used": 10, "days_in_period": 30, "prorated_charge": 30.0, "prorated_refund": 60.0, "answer": 30.0}.
Use when a plan starts, ends, or changes mid-period. Not for splitting a full amount into scheduled payments (installment_plan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need amount > 0 and 0 <= days_used <= days_in_period"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| days_used | Yes | Days of the period consumed; 0 to days_in_period. | |
| refund_mode | No | If true, "answer" is the refund amount instead of the charge. Default false. | |
| days_in_period | No | Length of the billing period in days; at least 1. Default 30. | |
| amount_per_period | Yes | Full-period price; must be greater than 0. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses critical behavioral traits beyond annotations: error mode ('never raises a protocol error — it returns an error object'), retry safety ('safe to retry after correcting input'), read-only and idempotent nature (already indicated by annotations but reinforced with concrete error context). The only minor gap is not explicitly stating the output schema structure beyond the example, but given the output schema exists, this is a high score.
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 two compact paragraphs: the first defines purpose, provides a prototypical example, and states when to use it versus a sibling. The second covers error behavior, safety, and idempotency. Every sentence adds distinct value with no filler. The technical example is front-loaded, making the core behavior immediately clear.
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?
Given the tool has 4 parameters (2 required), an output schema, and a simple deterministic mathematical function, the description fully covers the input/output contract, error handling, retry behavior, and usage boundaries. An AI agent has all the information needed to invoke the tool correctly, interpret results, and recover from errors.
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 description coverage is 100%, so baseline is 3. The description adds value by providing a full example showing how parameters map to the return values ('days_used', 'days_in_period', 'prorated_charge', etc.), and by explaining the default for 'days_in_period' (30) and the effect of 'refund_mode' (controls what 'answer' represents). This enriches the schema's own descriptions meaningfully.
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 states 'Compute a prorated charge or refund for a partial billing period' with a specific verb ('compute') and clear resource ('prorated charge or refund'). It includes a concrete example and explicitly distinguishes from the sibling tool 'installment_plan' by stating 'Not for splitting a full amount into scheduled payments'. This makes the purpose highly specific and differentiated.
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 explicit when-to-use guidance ('Use when a plan starts, ends, or changes mid-period') and explicit when-not-to-use guidance with a named alternative ('Not for splitting a full amount into scheduled payments (installment_plan)'). It also explains error handling behavior that guides retrying after fixing input, covering both usage and error recovery.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
charge_to_netdiscount_stackinstallment_planinvoice_totalprocessor_feesprorationClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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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.
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.
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.
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.