"mcp-pcb-emcopilot" matching MCP servers:

Python library for agent-operable experiment evidence and control. Bring any training code

Production-grade MCP server for EasyEDA Pro that enables AI-assisted hardware design review, PCB inspection, BOM sourcing, and manufacturing export through 41 profile-gated tools.

An MCP server that enables AI programming assistants to directly control JLCPCB EDA for PCB design automation, offering 39 tools for component placement, routing, copper pour, DRC, and a PCB Agent for autonomous complex tasks.

AI-powered PCB and schematic design with KiCad. Works with Claude, Cursor, VS Code, Claude Code, and any MCP-compatible client.

Enables agents to run statistics and data-science computations over numerical streams using only the Python standard library, including Z-score/IQR anomaly detection, Holt linear forecasting, Welch's t-test hypothesis evaluation, gradient-descent multivariate regression, and power-iteration SVD/PCA dimensionality reduction. Exposes these capabilities over JSON-RPC 2.0 stdio so clients like Claude Desktop, Cursor, and Windsurf can project high-dimensional feature vectors into principal components without any external dependencies.

MCP server for PathCourse Health enabling cheaper LLM inference, USDC-based agent payments, and agent identity/reputation lookup.

Exposes queryable GPU inference benchmark data (quantization, throughput, VRAM, concurrent users) as tools for LLM clients.

Enables agents to run zero-dependency statistical analysis natively over MCP, including Power Iteration PCA/SVD dimensionality reduction, Holt linear forecasting, modified Z-score and Tukey IQR anomaly detection, Welch's t-test, and multivariate gradient-descent regression on numerical data streams. Projects high-dimensional feature vectors into principal component representations without any external libraries.

Enables AI agents to run comprehensive Chinese convertible bond analysis with 21 signal detectors, option pricing, and backtest reports.

Audits KiCad PCB designs against South-East Asian fab capabilities using an IPC-compatible DFM rules engine with optional AI review via Ollama.

Framework for building bioengineering automation tools that AI assistants can call via the Model Context Protocol, handling server registration and tool discovery automatically.

Enables AI assistants to access and update personalized context data, creating persistent memory between sessions.