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scrutica_estimate_flops

Read-only

Estimate hypothetical peak and sustained throughput for a user-specified hardware configuration and precision. Sustained throughput equals dense peak throughput multiplied by accelerator count and utilization. The reported bounds are fixed at 0.7 and 1.15 times that result; they are model assumptions. Retain the bounds and is_estimated flag when quoting the estimate. This tool does not verify a named facility's inventory, availability or regulatory compliance. Named-facility estimates have separate evidence requirements documented at /methodology#flop-estimation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sparsityNoStructured-sparsity mode, accepted and echoed in the response. Both values currently apply a 1.0 multiplier.dense
precisionNobf16
unit_countYesNumber of accelerators
utilizationNoFraction of dense peak throughput sustained (0–1; default 0.40).
hardware_typeYesGPU / accelerator model

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / sparsity / description
      Previous value: -"Structured-sparsity mode. NOTE: both values currently apply a 1.0x multiplier — the theoretical 2x speedup of 2:4 sparsity rarely materializes in wall-clock training throughput, so this estimator deliberately does not credit it (see the response note and /methodology#flop-estimation). The parameter is accepted and echoed for forward compatibility only."New value: +"Structured-sparsity mode, accepted and echoed in the response. Both values currently apply a 1.0 multiplier."
    • changedInput schema / properties / utilization / description
      Previous value: -"Sustained utilization (MFU) fraction (default 0.40; documented range 0.20–0.50, calibrated to PaLM 540B 0.462 and LLaMA 3 405B 0.384)"New value: +"Fraction of dense peak throughput sustained (0–1; default 0.40)."
  2. Changed1 schema field changed
    • addedInput schema / properties / sparsity / description
      Added value: +"Structured-sparsity mode. NOTE: both values currently apply a 1.0x multiplier — the theoretical 2x speedup of 2:4 sparsity rarely materializes in wall-clock training throughput, so this estimator deliberately does not credit it (see the response note and /methodology#flop-estimation). The parameter is accepted and echoed for forward compatibility only."
  3. Changed2 schema fields changed
    • changedInput schema / properties / utilization / default
      Previous value: -0.65New value: +0.4
    • changedInput schema / properties / utilization / description
      Previous value: -"Sustained utilization fraction (default 0.65)"New value: +"Sustained utilization (MFU) fraction (default 0.40; documented range 0.20–0.50, calibrated to PaLM 540B 0.462 and LLaMA 3 405B 0.384)"
  4. First observed

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the readOnlyHint annotation. It discloses the calculation model (dense peak × unit_count × utilization), the fixed bounds (0.7 and 1.15), the instruction to retain the bounds and is_estimated flag, and its limitations regarding facility verification. This is rich behavioral context that a caller needs.

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?

Three sentences, zero filler. The purpose is front-loaded, the calculation and bounds follow, and the retention/limitations are compactly stated. Every sentence earns its place.

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?

The description covers the purpose, the calculation method, output bounds, the is_estimated flag, and points to methodology for named-facility edge cases. It lacks an explicit description of the full output structure (e.g., units, exact fields), but since the bounds and flag are mentioned, and no output schema exists, this is a minor gap. It is sufficiently complete for an agent to call it correctly.

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

Parameters4/5

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

With 80% schema description coverage, the baseline is 3. The description adds meaning to unit_count and utilization via the formula, and mentions precision and hardware configuration. However, it does not explain sparsity or precision beyond what the schema enums already convey, and the precision parameter lacks a schema description. The added formula justifies a 4, but it does not fully compensate for the undocumented precision parameter.

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's purpose: 'Estimate hypothetical peak and sustained throughput for a user-specified hardware configuration and precision.' It names the specific verb (estimate) and resource (throughput), and it is distinct from all sibling tools, which handle entities, facilities, or search. No ambiguity about what this tool does.

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 provides context for when to use it: it's for hypothetical estimates, and it explicitly states what it does NOT do (verify facility inventory/availability/compliance) and points to separate requirements for named-facility estimates. However, it does not name alternative tools or provide an explicit 'use this when...' statement, leaving some inference to the agent.

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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