Model Ruler — AI Cost Calculators

Quantization Calculator

quantization-calculator

Read-onlyIdempotent

Use when a user is planning to quantize an LLM to fit on smaller hardware. Given parameter count and precision transition, returns VRAM requirement, speedup estimate, and approximate quality delta.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "params_billions": 8
    },
    "name": "quantization-calculator"
  }
}

Input Schema

Name	Required	Description
`batch_size`	No	Serving batch size (default 1)
`precision_to`	No	Target precision (default int4)
`precision_from`	No	Starting precision (default bf16)
`kv_cache_tokens`	No	Max KV cache tokens (default 8192)
`params_billions`	Yes	Model parameter count in billions

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 6, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds useful output context by naming the three returned estimates, though it does not describe calculation assumptions or limitations.

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?

Two sentences, front-loaded with the usage condition and followed by the return values. Every sentence carries useful information without waste.

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?

Although there is no output schema, the description enumerates the return values (VRAM, speedup, quality delta), and the input schema fully details all five parameters. This is sufficient for an agent to invoke the calculator correctly.

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 parameter meanings are fully documented in the schema. The description only alludes to parameter count and precision transition, which adds little beyond what the schema already provides.

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 calculation: given parameter count and precision transition, it returns VRAM, speedup, and quality delta. This clearly distinguishes it from sibling cost and planning calculators.

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?

It explicitly gives the use case: when a user is planning to quantize an LLM to fit on smaller hardware. However, it does not mention when not to use it or name any alternative tools.

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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