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ltspice-mcp

ltspice-mcp

by xuio
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

getLocalExtrema

Identify local minima and maxima in simulation data vectors from LTspice runs.

Instructions

Get local minima/maxima for vectors.

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": {
      "vectors": "<vectors>"
    },
    "name": "getLocalExtrema"
  }
}

Input Schema

NameRequiredDescriptionDefault
vectorsYes
plotNo
run_idNo
raw_pathNo
step_indexNo
optionsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.32.0

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description must fully disclose behavioral traits. It implies read-only via 'Get', but does not confirm safety, side effects, or any required permissions. There is no mention of rate limits, data persistence, or other important behaviors.

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?

The description is a single concise sentence, but it is overly minimal. It lacks necessary details to be fully useful, so while it is front-loaded, it does not earn its place due to insufficient information.

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?

Given the tool has 6 parameters, no schema descriptions, no annotations, and an output schema, the description is far too sparse. It fails to clarify arguments, usage context, or return value, leaving agents without sufficient guidance.

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?

The schema has 0% description coverage, so the description should explain key parameters. It only names 'vectors' indirectly and omits details about 'plot', 'run_id', 'raw_path', 'step_index', and 'options'. The parameter names offer some hints, but the description adds no extra meaning.

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 action (Get), resource (local minima/maxima), and context (vectors). It effectively distinguishes itself from sibling analysis tools like getBandwidth or getRiseFallTime by specifying a unique function.

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 guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, such as requiring simulation data or specific vector names, nor any indication of 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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