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LTspice MCP

LTspice MCP

by kh6570
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Python
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Prepare Monte Carlo

prepare_monte_carlo

Set a schematic component to mc() using nominal and tolerance values, then step the run index for Monte Carlo simulation.

Instructions

Point a component at mc() and step the run index.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "schematic_path": "<schematic_path>",
      "reference": "<reference>",
      "nominal": "<nominal>",
      "tolerance": "<tolerance>"
    },
    "name": "prepare_monte_carlo"
  }
}

Input Schema

NameRequiredDescriptionDefault
runsNo
nominalYes
referenceYes
toleranceYes
schematic_pathYes
workspace_rootNoOptional per-call workspace root override.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.1

TDQS

D1.9/5.0
Behavior2/5

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

Annotations declare readOnlyHint=false and idempotentHint=false, so the agent knows this mutates state. "Step the run index" adds a fragment of behavioral context, but the description never says what on the schematic is modified, whether it is reversible, or what state it consumes and produces.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One short sentence is front-loaded, but it is under-specified rather than concise. The terseness omits information the agent needs, so brevity here does not earn its place.

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

Completeness1/5

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

For a 6-parameter mutation tool with no output schema and near-zero schema description coverage, the description is wholly inadequate. Nothing tells the agent what a valid call looks like or what to expect afterward.

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

Parameters2/5

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

Schema coverage is 17% across 6 parameters, so the description is expected to compensate and does not. It alludes loosely to a "component" and a "run index" but never explains reference, nominal, tolerance, runs, or schematic_path semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

"Point a component at mc() and step the run index" is cryptic shorthand: it never states what prepare_monte_carlo actually does to the schematic or how it differs from sibling run_monte_carlo. An agent must guess what "mc()" is and what "point a component at" means, so purpose is only obliquely conveyed.

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

Usage Guidelines2/5

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

No when-to-use, prerequisites, or alternatives are given. The "prepare_" prefix only weakly implies it precedes a run, and the description never routes the agent to run_monte_carlo (the obvious sibling) or distinguishes it from prepare_worst_case.

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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