"Building a Sequencer with Arduino in C++ and Enhancing Performance" matching MCP servers:

Enables AI clients to compile and upload Arduino sketches, manage boards, cores, and libraries via arduino-cli, scaffold projects, and read serial output over stdio.

A local stdio MCP server wrapping arduino-cli to let agents detect boards, manage cores/libraries, compile and upload sketches, and communicate over serial, enabling hardware control without shell gymnastics.

A comprehensive Model Context Protocol (MCP) server for Arduino CLI interactions, built with FastMCP. This server enables AI agents to seamlessly interact with Arduino CLI for development, debugging, code verification, and more.

MCP server for Arduino IDE 2.0 sketches. It exposes a small REST API plus an MCP stdio bridge so agents can read and write the main .ino file, list sources, and optionally compile with arduino-cli.

Enables AI assistants to compile, upload, and monitor Arduino boards via natural language, with electrical safety checks and dependency management.

Automates Arduino CLI workflows including dependency checks, compile/upload, serial monitoring, and safety preflight checks, all running locally on your machine.

Enables AI agents to detect boards, manage cores and libraries, compile and upload sketches, and perform serial I/O through a local MCP server.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Enables interaction with Arduino development through arduino-cli, allowing sketch management, code compilation and uploading, library and board operations, serial monitoring, and AI-powered circuit diagram generation using WireViz.

Analyzes websites like a senior performance engineer and produces prioritized Core Web Vitals recommendations. Provides 16 tools for detailed performance analysis, including Lighthouse, CrUX, WordPress and Cloudflare inspections.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

Enables AI agents to interact with Arduino boards for compiling, uploading sketches, and serial communication.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

Enables MCP clients to open websites, capture screenshots at multiple breakpoints, and run automated design audits for issues like overflow, small tap targets, low contrast, missing alt text, tiny text, and clipped content.

A high-performance, modular MCP server with safe-by-default profiles, read-only workspace access, search, diagnostics, and benchmarking tools, built with TypeScript and the MCP v2 SDK.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables AI agents to perform read-only static analysis of Node.js backend projects, detecting database query anti-patterns, async bottlenecks, connection pooling mistakes, and dependency hygiene issues while returning structured evidence-backed findings.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.