ltspice-mcp

by xuio

Overview Schema Source Related Servers Discussions Admin

Python

Local

readLtspiceUiText

Extracts visible text from LTspice windows using macOS Accessibility APIs for validating parser outputs and UI values.

Instructions

Read visible LTspice window text using macOS Accessibility APIs.

Use this to compare parser outputs against text displayed in LTspice UI (for example values shown in log windows).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "readLtspiceUiText"
  }
}

Input Schema

Name	Required	Default
`run_id`	No
`path`	No
`target`	No	log
`title_hint`	No
`exact_title`	No
`window_id`	No
`max_chars`	No
`open_if_needed`	No
`background`	No
`settle_seconds`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.32.0Jun 10, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Mentions the use of Accessibility APIs, hinting at system-level interaction. However, it does not disclose potential prerequisites (e.g., accessibility permissions), failure modes (e.g., if LTspice is not running), or whether the tool can block. With no annotations, more transparency is expected.

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

Conciseness5/5

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

The description is two sentences long, front-loaded with the core action, and includes a concrete example. Every sentence contributes value without redundancy.

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 high parameter count (10) and lack of annotations or output schema description, the description is insufficient. It covers the tool's purpose but leaves critical parameter behavior and return value assumptions undocumented, increasing the risk of misuse.

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 0%; the description does not explain any of the 10 parameters. For a tool with many parameters, the description provides no value beyond the schema, leaving the agent to infer semantics from parameter names alone.

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 action (read visible LTspice window text), the method (macOS Accessibility APIs), and a specific use case (compare parser outputs against displayed text). This distinguishes it from sibling tools like `getLtspiceUiStatus` which do different things.

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

Usage Guidelines4/5

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

Provides clear context on when to use (comparing parser outputs to UI text) with an example (log windows). However, it does not explicitly state when not to use or mention alternative tools for other UI-related tasks like `closeLtspiceWindow` or `openLtspiceUi`.

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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