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LTspice MCP

LTspice MCP

by kh6570
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Python
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Run Monte Carlo

run_monte_carlo

Prepare and run a Monte Carlo .step simulation in LTspice to test how schematic variations affect circuit behavior using reference, nominal, and tolerance inputs.

Instructions

Prepare a Monte Carlo .step and run it.

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": "run_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 already declare readOnlyHint=false, idempotentHint=false, openWorldHint=true, and destructiveHint=false. The description adds nothing beyond that — it does not say what artifacts are produced or overwritten, whether results are returned inline or written to disk, or anything about runtime cost for a Monte Carlo batch.

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

Conciseness3/5

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

It is a single front-loaded sentence with no wasted words, which is good. But the brevity crosses into under-specification for a 6-parameter mutation tool, so it reads as terse rather than efficiently complete.

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 non-idempotent, open-world mutation tool with no output schema and 4 undocumented required parameters, the description omits everything an agent needs — param formats, outputs, side effects, and how it relates to prepare_monte_carlo. It is far too thin for the tool's complexity.

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

Parameters1/5

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

Schema description coverage is only 17% (just workspace_root), and four required parameters (schematic_path, reference, nominal, tolerance) plus runs are entirely undocumented. The description provides no compensating meaning for any of them, so an agent cannot know the expected format of reference/nominal/tolerance.

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

Purpose3/5

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

The description names an action (run) and a resource (Monte Carlo analysis via a .step), so the basic purpose is inferable. However, 'Prepare a Monte Carlo .step and run it' is ambiguous — it implies this tool also prepares the analysis, which overlaps confusingly with the sibling prepare_monte_carlo, and it is not distinguished from run_worst_case, run_simulation, or the other run_* sweep tools.

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

Usage Guidelines1/5

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

There is no guidance on when to use this versus prepare_monte_carlo, prepare_step, run_worst_case, or the other sweep runners, despite a crowded sibling set. No prerequisites or ordering (prepare-then-run) are stated, leaving the agent to guess the workflow.

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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