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ossenna-hq

ltspice-mcp

by ossenna-hq

get_waveform

Read-onlyIdempotent

Retrieve waveform statistics from LTspice simulation results, returning a time-bucketed envelope with min, max, mean, RMS, peak-to-peak, and crest factor to analyze signal shape without downloading all samples.

Instructions

Decimated numeric egress FOR THE MODEL: returns a min/max-preserving stat-envelope of one real-valued signal as DATA in your context (numbers, not a picture) over a time/sweep/frequency window — for when a scalar isn't enough and you need the SHAPE (switching nodes, amplifier internal nodes, startup transients).

Splits the window into equal-time buckets; each bucket reports the raw sample min/max (a narrow spike or ringing peak is never averaged away), time-weighted trapezoidal mean/rms (correct on SPICE's adaptive timestep), pk_pk, and crest_factor (peak/rms — high = impulsive/spiky). Scalar-guided zoom: read the envelope, then re-request a narrower [t_start, t_end] to resolve a region at higher resolution (same call, tighter window). The observations list surfaces FACTS, not verdicts (decimation coverage, dropped non-finite samples, which bucket has the largest pk-to-pk) — you decide what the shape means.

Works on transient (.tran), DC sweep (.dc), and noise (.noise) results. Sibling egress, don't confuse: export_waveform writes EVERY sample to a CSV FILE for your own code; plot_waveform renders an interactive PICTURE for a human to look at. For complex AC data use bode_metrics; for a single scalar use signal_stats; for one point value use query_value.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepNoStep index for .step directives.
t_endNoWindow end in SPICE notation.
formatNoResponse format: 'json' for structured data, 'text' for human-readable
job_idNoDecimate a specific run of a completed sweep/MC (or single) job instead of a raw_file path; pair with ``run_index``.
signalYesSignal/trace name (e.g., 'V(out)', 'I(R1)'), or a device operating-point shorthand for an ngspice .save'd parameter: 'm1.gm' / 'm1.vth' (resolves to '@m1[gm]', incl. subcircuit paths like 'x1.m1.gm').
bucketsNoNumber of equal-time envelope buckets (overview resolution). Defaults to 200; capped at 2000 (and at the server's max_points_returned ceiling and the sample count).
t_startNoWindow start in SPICE notation (e.g. '1m', '100u'). Narrow the window and re-request to zoom into a region of interest.
raw_fileNoPath to .raw result file. Pass this OR ``job_id`` (a job run), not both.
run_indexNo0-based run to read when ``job_id`` is given (default 0).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalNo
bucketsNo
axis_unitNo
decimatedNo
point_countNo
bucket_countNo
observationsNo
analysis_typeNo
window_end_usedNo
window_start_usedNo
max_points_ceilingNo
Behavior5/5

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

The description discloses the internal operation: splitting into buckets, reporting min/max, time-weighted mean/rms, pk_pk, crest_factor, and surfacing facts like decimation coverage and dropped non-finite samples. It also notes the default bucket count and cap. The annotations already indicate read-only/idempotent, and the description adds no contradictions.

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 long but well-structured, with three paragraphs: purpose and use cases, bucket mechanics, and output semantics/contrast with siblings. It is front-loaded with the primary purpose and every sentence adds value—no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the description thoroughly covers purpose, usage, parameter interactions, output behavior, and contrasts with related tools. It also notes the output schema exists (though not shown) and explains the envelope concept and zoom workflow, making it self-sufficient for an agent to decide when and how to use it.

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

Parameters4/5

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

The schema already has high coverage and detailed descriptions for each parameter. The description adds usage context by explaining how parameters interact (e.g., t_start/t_end to zoom, raw_file vs job_id mutually exclusive, bucket count cap), which goes beyond individual schema descriptions. However, it does not introduce new parameter-level details beyond what the schema provides.

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 that the tool returns a min/max-preserving stat-envelope of a signal over a time window, and distinguishes it from siblings like export_waveform (CSV) and plot_waveform (picture). It specifies the verb 'returns' and the resource 'waveform envelope' with concrete use cases (switching nodes, amplifier nodes, startup transients).

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

Usage Guidelines5/5

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

The description explicitly tells when to use this tool ('when you need the shape') and when not to (for complex AC use bode_metrics, for scalar stats use signal_stats, for one point use query_value). It also explains the zoom workflow (narrow window and re-request).

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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