Artificial intelligence is beginning to transform FPGA verification and automated test workflows. In a recent article, FPGA engineer Pablo Trujillo demonstrates how an AI agent can control a Red Pitaya STEMlab PRO Gen 2 using the Model Context Protocol (MCP) to generate signals, configure test equipment, acquire measurements, and verify FPGA designs with minimal manual intervention.
The project shows how combining AI agents, FPGA-based instrumentation, and SCPI-controlled test equipment can simplify complex verification tasks while reducing repetitive engineering work.
Instead of manually writing SCPI commands, Python scripts, and test procedures, the AI agent uses a custom Red Pitaya MCP server to communicate directly with the instrument. Through MCP, the AI can configure waveform generation, trigger acquisitions, capture measurement data, and automate verification workflows using natural language.
The demonstration combines a Red Pitaya STEMlab PRO Gen 2 with a Digilent USB104 A7 FPGA board. During the workflow, the AI agent generates a digital bandpass filter, builds the FPGA design on a remote Vivado server, programs the hardware, and verifies the implementation by controlling Red Pitaya as both a signal generator and measurement instrument.
One of the key takeaways is how the Model Context Protocol (MCP) abstracts complex instrument control. Rather than requiring engineers to remember individual SCPI commands and firmware-specific behavior, the MCP server exposes Red Pitaya functions as reusable AI tools. This enables AI assistants such as Claude Code and other MCP-compatible clients to automate repetitive laboratory and FPGA verification tasks more reliably.
The article also discusses how open-source instrumentation, standardized interfaces such as SCPI, and AI-assisted development can help engineers accelerate hardware validation while maintaining the flexibility needed for embedded systems, RF applications, and digital signal processing projects.
For engineers interested in AI-assisted FPGA development, automated test and measurement, or integrating Red Pitaya into AI-driven engineering workflows, the complete article provides a detailed walkthrough of the architecture, implementation, and lessons learned.
Read the full article by Pablo Trujillo on ControlPaths.
Red Pitaya MCP is a custom implementation of the Model Context Protocol (MCP) that allows AI agents to control Red Pitaya instruments through structured tools instead of manually issuing SCPI commands.
In the demonstrated workflow, the AI agent generates FPGA logic, configures the Red Pitaya STEMlab PRO Gen 2, performs signal generation and data acquisition, programs the FPGA, and verifies measurement results with minimal human intervention.
The project combines a Red Pitaya STEMlab PRO Gen 2 with a Digilent USB104 A7 FPGA development board. Red Pitaya performs high-speed signal generation and acquisition, while the FPGA executes the digital filter implementation.
MCP simplifies communication between AI assistants and laboratory instruments by exposing common measurement functions as standardized tools. This reduces the complexity of automation scripts and makes instrument control more reliable.
The complete technical walkthrough, including the MCP server implementation and FPGA verification workflow, is available on the ControlPaths website:
https://controlpaths.com/2026/07/18/red-pitaya-stemlab-mcp-ai-verification-bench/