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NeuronCState

Digital MCP Server

by NeuronCState

digital_export_simulation_image

Simulate a Digital logic circuit with custom input states and export a high-resolution SVG or PNG snapshot of the rendered model, enabling visual verification without a GUI.

Instructions

Apply input/button states such as {A: 1, B: 0}, render the live Digital model, and export a high-resolution SVG or PNG snapshot. The circuit is simulated headlessly; the GUI is not driven.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
scaleNo
formatNopng
inputsNo
hide_testNo
output_pathNo
pixel_widthNo
timeout_secondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does say the circuit is simulated headlessly and the GUI is not driven, but it does not disclose whether applying inputs mutates the model, whether output_path is required for file creation, what side effects occur, or what resource limits apply.

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 with no filler. It front-loads the core behavior (apply states, render, export) and follows with the useful GUI exception, making each sentence earn its place.

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 tool has eight parameters, nested objects, no output schema, and no parameter descriptions, the description is too thin to be fully complete. It does not explain what path refers to, how the output is returned or saved, what hide_test means, or how inputs interact with an already-open Digital model.

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 0%, so the description must compensate. It gives one useful example for the inputs parameter and mentions SVG/PNG output, but the majority of the eight parameters—path, output_path, scale, pixel_width, hide_test, timeout_seconds—receive no semantic clarification in either the schema or the description.

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

Purpose4/5

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

The description clearly states a specific action: apply input states, render the live Digital model, and export an SVG/PNG snapshot. It is more specific than the tool title and conveys the headless simulation nature, though it does not explicitly call out sibling differentiators like digital_export_rendered_image.

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

Usage Guidelines3/5

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

The description implies when to use the tool: when you need a simulation snapshot with applied input states, and it notes that the GUI is not driven. However, it does not explicitly state when to prefer this tool over the related export/render siblings or give concrete exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.