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LTspice MCP

LTspice MCP

by kh6570
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Python
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Measure Efficiency

measure_efficiency
Read-onlyIdempotent

Computes efficiency by dividing average output power by average input power from voltage and current waveform data.

Instructions

Average output power over average input power.

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": {
      "raw_path": "<raw_path>",
      "v_in": "<v_in>",
      "i_in": "<i_in>",
      "v_out": "<v_out>",
      "i_out": "<i_out>"
    },
    "name": "measure_efficiency"
  }
}

Input Schema

NameRequiredDescriptionDefault
i_inYes
v_inYes
i_outYes
v_outYes
raw_pathYes
workspace_rootNoOptional per-call workspace root override.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.1

TDQS

D1.8/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds only the computation formula and discloses nothing new about behavior, such as whether raw_path must contain a specific simulation output, unit expectations for the voltage/current arguments, or what triggers an error. A 2 reflects that it carries no behavioral context beyond the annotations.

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 short sentence with zero padding, which is structurally clean. However, it is under-specified rather than genuinely concise — the brevity comes at the cost of the information an agent needs before invoking the tool.

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?

With six parameters, 17% schema coverage, no output schema, and no annotations explaining inputs, the description is far too thin. It does not explain the raw-file dependency, the units or expected form of the four electrical arguments, or what result (a single efficiency number?) is returned, so an agent could easily call it incorrectly.

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 description coverage is 17% (only workspace_root is documented), so the description must compensate and does not. The names v_in/i_in/v_out/i_out loosely imply input/output voltages and currents, but no syntax, unit, format, or role for raw_path is given anywhere. This leaves five required parameters essentially undocumented.

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?

The description is a textbook definition of the efficiency metric rather than a statement of what the tool does. It never mentions that the tool reads a raw waveform file (raw_path) and derives efficiency from the supplied voltage/current series, so an agent cannot tell what action is performed beyond the metric name. It effectively restates the title 'Measure Efficiency'.

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 when-to-use guidance, no prerequisites, and no routing to alternatives. Given siblings like measure_waveform, measure_bode_response, compute_thd, and read_measures, an agent gets no help deciding when efficiency measurement is the right call over these other measurement tools.

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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