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FiscaLLM

by io.github.magna56
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Break-even usage for a subscription price

subscription_break_even
Read-onlyIdempotent

How many AI messages a month a subscriber can send before the model cost uses up what the plan earns, after the store or payment fee. Answers: does this plan price cover this model?

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>",
      "monthlyPriceUsd": "<monthlyPriceUsd>"
    },
    "name": "subscription_break_even"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYesModel id.
feePercentNoStore or payment fee. App stores take 15 to 30; Stripe about 3.
inputTokensNoPrompt tokens per message (system prompt and history).
cachedTokensNoHow many of the prompt tokens are read from the prompt cache.
outputTokensNoReply tokens per message.
monthlyPriceUsdYesThe plan's price per month.
messagesPerMonthNoOptional: a user's expected messages a month, to get that user's margin.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description adds that the fee is deducted and that per-message token assumptions drive the result, but says nothing about the output shape (break-even count, whether margin is returned when messagesPerMonth is supplied).

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

Conciseness4/5

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

Two sentences, front-loaded with the core computation and zero filler. The first sentence is slightly convoluted ('uses up what the plan earns'), which costs a bit of immediate clarity but the structure is sound.

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

Completeness3/5

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

Seven parameters and no output schema, so the description carries more burden than it takes on: it never states what the response contains, particularly the extra margin figure implied by the optional messagesPerMonth parameter. The core intent is complete, but return semantics are left to inference.

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?

Schema description coverage is 100%, so every parameter is already documented with defaults and ranges. The description only reinforces that feePercent is applied after revenue, adding minimal meaning beyond the schema; baseline 3 applies.

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

Purpose4/5

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

The description states a specific computation: the monthly message count at which model cost consumes plan revenue, net of store/payment fees. It is clearly distinguishable from pricing-lookup or aggregation siblings such as get_llm_price or get_margin_overview, though it does not name an alternative directly.

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?

Usage is implied by the closing question 'does this plan price cover this model?', which suggests evaluating plan viability. There is no explicit when-to-use versus alternatives guidance and no exclusions or prerequisites stated.

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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