get_waveform with 3 AC signals at 700 points produced ~92K chars of JSON,
exceeding MCP client token limits. Three fixes:
- Round output values (dB to 2dp, phase to 2dp, freq/time to 6 sig figs),
cutting JSON size ~63% (97K -> 36K chars for the same data)
- Peak-preserving downsampling for AC data: bucket-based selection keeps
the point with the largest magnitude deviation per bucket, so narrow
resonance peaks (high-Q filters) aren't lost by blind stride sampling
- Add x_min/x_max parameters to get_waveform and evaluate_waveform_expression
for zooming into a frequency/time range without excessive global point counts
Row-major data from raw_parser was indexed as column-major, producing
garbled plots for digital waveforms. Also adds signals parameter for
overlaying multiple time-domain traces with legend, and adaptive X-axis
labels (ns/µs/ms/s) based on time span.
Expose x_min/x_max/y_min/y_max, max_points, width/height, and title
override through the MCP tool. Data is clipped to X range before
stride-based downsampling for better zoomed resolution. All params
default to None/current behavior for backward compatibility.
Rename package from mcp-ltspice/mcp_ltspice to mcltspice throughout:
source directory, imports, pyproject.toml, tests, and README.
Remove startup banner prints from main() since FastMCP handles
its own banner and stdout is the MCP JSON-RPC transport.
Point repo URL at git.supported.systems/MCP/mcltspice.
