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by Tencent · MCP Server · ★ 199
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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wave-mcp：开源、免 License 的 RTL 波形调试 MCP Server English | 简体中文 wave-mcp 是腾讯蓬莱实验室验证团队开源的一款 RTL 波形调试 MCP Server，为 LLM 提供波形调试工具集： 读 FST 波形 + RTL 网表，提供层次探索、信号查询、驱动分析、值/X 态追踪、波形对比与浏览器波形查看器等 34 个 MCP 工具。 MIT 开源，无需任何商用 License，支持任意并发。 FST 直读，VCD / FSDB 自动转 FST：Verilator 、Icarus 直接产 FST 就能读； 手上只有 VCD 或 FSDB 也没关系， 自动转换后再建 session（FSDB 转换不占 Verdi license）。 它不跑仿真器，你用自己的流程跑出波形，把结果交给它即可。 为什么是 wave-mcp 芯片验证占据开发周期 50% 以上的时间，波形调试是其中最高频的动作。而 LLM 时代， 工程师希望让 AI Agent 直接读波形、查信号、追 X 态根因，但市面上的商用调试 MCP 需要昂贵的 License，且并发受限。 wave-mcp 用纯开源技术栈（pylibfst + pyslang）提供完整波形调试能力： 免 License、数据准确、真实芯片项目背书。 生产级验证 在真实生产级芯片项目（几十个模块）上完整验证，并把 OpenTitan、香山纳入测试集： 特性 波形查询：设计层次、实例、信号（位宽/方向/
| Stars | 199 |
| Forks | 26 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 64.8034753779669/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-11 |
| Created | 2026-07-27 |
| Platforms | browser, mcp, python |
| Est. Tokens | ~17k |
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wave-mcp is Open-source, license-free MCP server for RTL waveform debug: reads FST waveforms (VCD/FSDB auto-convert) plus a SystemVerilog netlist, with 34 tools covering driver analysis, value/X tracing, pass-fai. It is categorized as a MCP Server with 199 GitHub stars.
wave-mcp is primarily written in Python. It covers topics such as chip-verification, debug, eda.
You can find installation instructions and usage details in the wave-mcp GitHub repository at github.com/Tencent/wave-mcp. The project has 199 stars and 26 forks, indicating an active community.
The top alternatives to wave-mcp on Agent Skills Hub include TraceWeave, mcp-nixos, llm-functions. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: