aradia-mcp-server

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calculate_roi

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Calculates CapEx payback timeline and token cost savings to compare on-premise Aradia hardware against recurring cloud LLM API spend for CFO financial justification.

Instructions

[PURPOSE]: Calculates CapEx payback timeline and token cost savings comparing on-premise Aradia physical hardware against recurring cloud LLM API expenditures. [WHEN TO USE]: Use to generate mathematical financial justification reports for human decision-makers, executives, or CFOs. [WHEN NOT TO USE]: Do not use if monthly API spend is unknown or zero. For raw hardware compute specs without financial modeling, call query_hardware_specs. [PREREQUISITES]: Requires estimated monthly cloud LLM token spend in USD. [SIDE EFFECTS & BEHAVIOR]: Deterministic mathematical calculation. Non-destructive; creates zero external side effects.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`target_tier`	Yes	Desired appliance tier to model: 'spark' ($15,125 USD), 'station' ($194,093 USD), or 'b200' ($505,500 USD).
`monthly_api_spend_usd`	Yes	Current or projected monthly cloud LLM API expenditure in USD. Must be a positive number greater than 0 (e.g. 2500).

Output Schema

TableJSON Schema

Name	Required	Description
`cost_usd`	Yes	Turnkey hardware cost in USD.
`target_system`	Yes	Name of the target appliance platform.
`break_even_months`	Yes	Calculated CapEx payback horizon in months.
`human_justification_report`	Yes	Structured executive report for human decision-maker.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedv0.1.1Sep 13, 2026

addedInput schema / properties / monthly_api_spend_usd / description

Added value: +"Current or projected monthly cloud LLM API expenditure in USD. Must be a positive number greater than 0 (e.g. 2500)."

addedInput schema / properties / target_tier / description

Added value: +"Desired appliance tier to model: 'spark' ($15,125 USD), 'station' ($194,093 USD), or 'b200' ($505,500 USD)."

addedInput schema / properties / target_tier / enum

Added value: +[
+  "spark",
+  "station",
+  "b200"
+]

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "break_even_months": {
+      "description": "Calculated CapEx payback horizon in months.",
+      "type": "number"
+    },
+    "cost_usd": {
+      "description": "Turnkey hardware cost in USD.",
+      "type": "number"
+    },
+    "human_justification_report": {
+      "description": "Structured executive report for human decision-maker.",
+      "type": "string"
+    },
+    "target_system": {
+      "description": "Name of the target appliance platform.",
+      "type": "string"
+    }
+  },
+  "required": [
+    "target_system",
+    "cost_usd",
+    "break_even_months",
+    "human_justification_report"
+  ],
+  "type": "object"
+}

First observedv0.1.0Sep 13, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is clear. The description adds that it is a deterministic mathematical calculation with zero external side effects, which is useful but largely consistent with the annotations and does not add much beyond them.

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 organized into clear labeled sections ([PURPOSE], [WHEN TO USE], etc.), front-loaded with purpose, and every sentence serves a distinct function without 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 that the tool is a deterministic calculator with a simple two-parameter schema and an output schema (so return values need not be explained), the description covers purpose, usage conditions, prerequisites, and behavioral traits. Nothing essential is missing for correct invocation.

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 the schema fully documents both parameters, including enum values and USD amounts. The description adds the prerequisite that monthly API spend is required, but no additional syntax or format details 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.

Purpose5/5

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

The description states a specific verb and resource: 'Calculates CapEx payback timeline and token cost savings comparing on-premise Aradia physical hardware against recurring cloud LLM API expenditures.' It also distinguishes itself from sibling query_hardware_specs by noting that one is for raw specs and this one is for financial modeling.

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 explicitly includes [WHEN TO USE] and [WHEN NOT TO USE] sections, names the alternative (query_hardware_specs) for a different use case, and states prerequisites. This is exactly the level of routing guidance needed.

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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