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configure_montecarlo

Idempotent

Set up Monte Carlo analysis by specifying component tolerances, process variations, and mismatch parameters for a netlist file. Returns a configuration ID for later execution.

Instructions

Configure a Monte Carlo analysis with component tolerances and return a config_id for later execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
netlistYesPath to the netlist file (.cir, .net, .asc)
tolerancesNoR/C/L (and V/I type-level) component tolerance specifications.
model_tolerancesNoProcess-variation rules: per-.MODEL parameter perturbations sampled once per run. All instances of the model see the same perturbation (correlated).
mismatchNoPelgrom-law mismatch rules per device prefix. Sampled INDEPENDENTLY per instance per run. Requires explicit AVT/AK — defaults are 0 (no mismatch) since coefficients are technology-specific.
param_tolerancesNoSample-once-per-run perturbation of .PARAM directives. Use this when the netlist already wires {param} substitutions into model cards or component values.
num_runsNoNumber of Monte Carlo iterations
seedNoOptional RNG seed for reproducible runs. None = fresh entropy each call.
Behavior3/5

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

Annotations already indicate idempotentHint=true, readOnlyHint=false, destructiveHint=false. The description adds it returns a config_id but doesn't elaborate on side effects, persistence, or prerequisites.

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 single-sentence description is concise and front-loaded with the verb and resource, but could be slightly improved by noting the sibling relationship.

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?

Given the complexity (7 parameters, nested objects, no output schema), the description is too brief. It lacks an overview of the tolerance types and workflow, relying entirely on parameter descriptions.

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 thorough parameter descriptions, so the description doesn't need to add detail. It provides no additional meaning beyond the schema.

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 verb 'configure' and the resource 'Monte Carlo analysis', and distinguishes from the sibling 'run_montecarlo' by noting it returns a config_id for later execution.

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 that this tool should be used before 'run_montecarlo', but lacks explicit guidance on when to use vs. alternatives (e.g., configure_sweep) or when not to use it.

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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