FilterForge: An LLM-Based, Semi-Automated Agentic VS Code Extension for Microwave Bandpass Filter Design
APPLIED SCIENCES, vol.16, no.13, pp.1-45, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 16 Issue: 13
- Publication Date: 2026
- Doi Number: 10.3390/app16136379
- Journal Name: APPLIED SCIENCES
- Journal Indexes: Applied Science & Technology Source, Scopus, Science Citation Index Expanded (SCI-EXPANDED), Compendex, INSPEC, Directory of Open Access Journals
- Page Numbers: pp.1-45
- Open Archive Collection: AVESIS Open Access Collection
- Erzincan Binali Yildirim University Affiliated: Yes
Abstract
We present FilterForge, a chat-driven VS Code environment that pulls the synthesis, analysis, simulation, and optimization stages of microwave bandpass filter design, normally coordinated by hand across tools written in different languages, into one workflow. A deployed Model Context Protocol (MCP) server exposes deterministic Python implementations of coupling-matrix synthesis, uniform predistortion, topology reconfiguration, a genetic-algorithm transmission-zero selector, a mode-matching engine for H-plane iris-coupled rectangular waveguide geometries, and a skill that generates PyAEDT/HFSS notebooks for various dimensioning design-curves. A language-model orchestrator turns natural-language requests into typed tool calls, while every reported quantity stays inside the deterministic kernels, so the numerics remain reproducible and model-agnostic. We evaluate the call layer on a 45-task benchmark across the five tool categories: gemini-3-flash reaches 96.3%