Skip to main content
Glama

calculators

AI Tool ROI Calculator (Payback on Adoption)

ai_roi_calculator

AI Tool ROI Calculator (Payback on Adoption) — Estimate the ROI and payback of an AI tool: enter hours saved per week, staff count, and cost to see monthly net savings, break-even months, and annual return.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
headcountYes
hourlyCostYes
monthlyToolCostYes
hoursSavedPerWeekYes
implementationCostYes

TDQS

B3.1/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the transparency burden. It is upfront about the calculation scope (monthly net savings, break-even months, annual return) and inputs, but it does not disclose the underlying formula or assumptions, such as how implementation cost is treated or whether the calculation is per-staff or aggregate.

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?

The description is a single efficient sentence that packs in the purpose, inputs, and outputs without unnecessary detail. It loses a point for opening with a near-verbatim repetition of the tool's title before getting to the actionable 'Estimate' clause.

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

Completeness2/5

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

For a 5-parameter required-input calculator with no output schema and no annotations, the description is not complete enough. It lists outputs but fails to define all input parameters clearly, and provides no guidance on how break-even or annual return are derived, which is needed for confident invocation.

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

Parameters2/5

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

The schema has 5 required parameters with 0% description coverage, so the description must compensate. It mentions 'hours saved per week, staff count, and cost,' which roughly maps to hoursSavedPerWeek, headcount, and monthlyToolCost, but leaves hourlyCost and implementationCost semantically ambiguous and does not clarify whether 'cost' refers to monthly tool cost or combined costs.

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 clearly states the action ('Estimate the ROI and payback of an AI tool') and names the specific resource. It also lists key inputs and outputs, which helps distinguish it from generic ROI calculators, though it does not explicitly differentiate it from very similar siblings like ai_automation_payback_calculator.

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?

The description implies usage by specifying the required inputs (hours saved per week, staff count, cost) and outputs, but it provides no explicit guidance on when to choose this tool over alternatives such as generic roi_calculator, break_even_calculator, or ai_automation_payback_calculator, nor any exclusions.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.1/5.0
Disambiguation2/5

Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.

Naming Consistency5/5

Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.

Tool Count1/5

122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.

Completeness3/5

The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.

Resources