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calculate_pace_layer_drag

Calculate the annual financial cost of organizational drag caused by misalignment between AI adoption tier and readiness level. Quantifies hidden structural friction costs.

Instructions

Calculate annual Organisational Drag Cost in EUR from misalignment between AI tier and organisational readiness. The hidden cost of structural friction, not the cost of the AI build. Use to quantify the cost of NOT changing the operating model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
revenue_eurYesApproximate annual revenue in EUR.
ai_tierYesgen1=automation/RPA, gen2=GenAI, gen3=agentic.
readinessYesOrganisational readiness. Honest self-assessment.
industryNoOptional, for future vertical adjustments.
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that it calculates a cost from misalignment and clarifies it's not the build cost. It does not mention authentication, rate limits, or side effects, which is acceptable for a calculation tool but still leaves some gaps.

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 three sentences, front-loading the primary purpose. Each sentence adds value: clarifying what the cost is, what it is not, and when to use it.

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?

Given no output schema, the description implicitly indicates the output is a number in EUR for annual cost. However, it does not specify the exact format or additional details about the result, leaving some uncertainty for a calculation tool.

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 coverage is 100%, meaning the input schema already describes all parameters with enums and explanations. The description adds minimal additional meaning beyond the schema, such as hinting that 'readiness' requires honest self-assessment. 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 clearly states 'Calculate annual Organisational Drag Cost in EUR from misalignment between AI tier and organisational readiness.' It specifies the verb, resource, and differentiates from siblings by clarifying it quantifies the cost of NOT changing, not the build cost.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use to quantify the cost of NOT changing the operating model,' providing clear context. However, it does not explicitly mention when not to use or name specific alternatives, though the sibling tools context suggests differentiation.

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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