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get_savings

Get model-swap recommendations that reduce costs while maintaining quality, with projected monthly savings and adoption status.

Instructions

List model-swap recommendations the platform thinks would save money without losing quality. Each item carries projected monthly savings, prior-window cost, and an achieved flag if the swap has already been adopted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNoAnalysis window in hours. Default 168 (7 days). Longer = more confident, but slower.
minSavingsNoOnly return recommendations projecting at least this many USD/month in savings.
Behavior3/5

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

No annotations provided, so description must compensate. It names fields in items (savings, cost, achieved flag) but omits side effects, rate limits, data freshness, or safety assurances typical for a read operation.

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 two sentences, each adding value. No redundant information, and it is front-loaded with the core purpose.

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

Completeness4/5

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

Given no output schema, the description explains item fields adequately. It could mention ordering or defaults but is complete for a simple list tool. Siblings are distinct, reducing confusion.

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% with clear descriptions for both parameters. The tool description adds no additional meaning beyond the schema, so baseline score of 3 applies.

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 the tool lists model-swap recommendations for cost savings without quality loss. It distinguishes from siblings like get_anomalies, get_stats, etc., which serve different purposes.

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 for cost-saving recommendations but does not explicitly state when to use this tool versus alternatives or provide any when-not-to-use guidance.

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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