"Two agents working simultaneously" matching MCP servers:

Provides AI agents with instant, structured access to electronic component datasheets, pinouts, and electrical specifications without requiring PDF uploads. It enables seamless part searching, design validation, and side-by-side component comparisons across major hardware providers.

Enables AI agents to control serial port devices (modems, instruments, embedded boards) via MCP tools for listing ports, connecting, and sending/receiving commands.

MCP server providing embedded engineering calculators and code generators as tools for AI agents, enabling precise, deterministic embedded math and C code generation.

Enables controlling multiple USBTMC (USB Test and Measurement Class) devices simultaneously using SCPI commands through a unified MCP interface.

Serial and GDB/SWD MCP servers for real hardware: agents read consoles, drive U-Boot, set breakpoints, and probe unknown Cortex-M boards.

Enables AI agents to compile, flash, erase, and read serial output from embedded development boards, with guardrails such as budget limits, serial port mutexes, and mandatory human confirmation for destructive operations.

Enables AI agents to interact with physical embedded target boards over a dual-CDC USB bridge using MCP tools. It exposes console reading, pattern waiting, command sending, reboot/login state machines, and in-RAM MicroPython execution while a single-owner daemon safely arbitrates access to the serial devices.

Enables AI agents to design hardware by writing C-like HDL and compiling it to Verilog, with real toolchain verification including synthesis checks.

An MCP server that enables AI agents to operate an oscilloscope through high-level tools like signal capture and measurement, abstracting vendor-specific SCPI commands.

An MCP server that lets AI agents control an RS PRO RS-3005P/RS-6005P programmable DC power supply over USB/RS232, enabling voltage/current control, measurements, output switching, and memory operations.

Enables AI agents to control and display real-time notifications, telemetry, and custom apps on Ulanzi TC001 and compatible pixel clocks running Awtrix Light, including device management and declarative YAML-based metric polling.

MCP server for managing large fleets of BLE devices with connection pooling, scheduling, retries, and partial-failure handling. Enables AI agents to run fleet-level reads/writes without worrying about radio connection limits.

Allows AI agents to interact with serial devices via RS232/UART, enabling port listing, connection, read/write, control line manipulation, and protocol specification for automated debugging and testing.

Enables an AI agent to control one or more remote GPIO agents through a conversation by proxying tool calls to their REST APIs. It supports API-key authentication, LAN discovery, and both stdio and Streamable HTTP transports.

MCP server enabling AI agents to search FTC SDK samples, scaffold OpModes, build and deploy TeamCode to REV Control Hub, and read robot logs for a full code-robot-debug loop.

A Model Context Protocol (MCP) server for ROS 2 that enables GitHub Copilot and other AI agents to interact with ROS 2 systems. This server provides tools for monitoring, debugging, and managing ROS 2 nodes, topics, services, and TF2 frames.

A stateful Bluetooth Low Energy (BLE) MCP server that enables AI agents to scan, connect, read/write characteristics, and subscribe to notifications on BLE devices.

Enables agents to control a JUNTEK JDS2800 USB signal generator over serial, configuring frequency, waveform, amplitude, offset, duty, phase, sweeps, arbitrary waveforms and independent output enables, with verified readback. It also provides bundled manual reference material and a measured 50-ohm sine power calibration so requested dBm levels can be set and previewed within the calibrated ranges.

A read-only MCP server for AI agents to understand KiCad projects through progressive disclosure, providing compact summaries and drill-down tools for components, nets, traces, and ERC/DRC checks without blowing context budgets.

Enables AI agents to perceive and quantitatively verify physical hardware through USB webcams and RTSP/HTTP network streams, returning inline image content plus numerical metrics such as brightness, contrast, edge density, motion, SSIM baseline comparisons, and OCR text. Agents can watch feeds for visual conditions, correlate buffered frames with serial/log/MQTT events, and apply privacy masks and access controls.