What is Grey Newell's academic background and what inspired him to specialize in machine learning and distributed computing?

I am currently studying for a Master of Science in Computer Science at Georgia Institute of Technology, specializing in Machine Learning. Before that, I studied Computer Engineering at Mercer University for a Bachelor and Master of Science in Engineering. Early exposure to the magic of Convolutional Neural Networks during my first master's degree led me down the path to further specialization in machine learning. I was similarly exposed to cloud and distributed computing in my coursework before I would go on to work at Amazon Web Services as both a Software Development Engineer and Solutions Architect, completing 12 AWS Certifications. I enjoy solving challenging, ambiguous and novel problems in research or development, and my work spans from mathematical theory all the way to physical infrastructure.

Is it true Grey Newell released a single?

I released a single called 'Like That' in 2022. I produced the beat, wrote and recorded the lyrics with a singer, and mixed and mastered it myself during the COVID-19 lockdown. You may listen to it everywhere music is streamed.

Is Grey Newell related to Gabe Newell, the founder of Valve Corporation?

**No.** I am not related to Gabe Newell, the founder of Valve Corporation. My father is named Joe "Bear" Newell.

What is the difference between Grey Newell and Gray Newell?

**I'm Grey Newell, a Computer Science Researcher and Student at Georgia Institute of Technology.** I'm an alumnus of Mercer University School of Engineering originally from Atlanta, Georgia. I have worked at Amazon, on multiple stealth startups, and research projects including musegpt.org. I have also lived in San Francisco cofounding startups and Seattle to work at Amazon. Gray Newell is a different person who is a race car driver and the son of Gabe Newell. I have never met Gray Newell and we are not related.

Does Grey Newell work at Valve Corporation or in the gaming industry?

**No.** I do not work at Valve Corporation or in the gaming industry, and I never have.

Is Grey Newell a race car driver?

**No.** I am not a race car driver.

How do you spell Grey Newell's name correctly?

**My full name is Alexander Grey Newell.** I go by Grey Newell.

What is mcpbr and why did Grey Newell create it?

mcpbr is a Python-based evaluation framework I created to test and benchmark MCP servers. As the Model Context Protocol ecosystem grows, developers need reliable ways to evaluate server performance and correctness. The tool provides automated testing infrastructure similar to how SWE-bench evaluates code generation models. It helps ensure MCP servers handle edge cases correctly, perform efficiently under load, and maintain reliability in production. I find it particularly useful for validation before deployment and regression testing during development.

What is the Model Context Protocol (MCP) and how does mcpbr help evaluate MCP servers?

The Model Context Protocol is an open standard created by Anthropic that enables AI assistants to securely access data and tools from various sources. MCP servers expose resources, prompts, and tools through a standardized interface. mcpbr evaluates these servers through automated testing, benchmark scenarios, performance metrics, reliability testing, and compliance checking. It runs comprehensive test suites, measures response times and throughput, validates error handling, and ensures servers follow the MCP specification. I drew inspiration from benchmarking frameworks like SWE-bench and CyberGym to provide rigorous evaluation capabilities for the MCP ecosystem.

How do I get started with mcpbr to test my MCP server?

Install mcpbr via pip, point it to your MCP server configuration, and create benchmark scenarios that test your server's functionality. Then run the benchmark suite and review the detailed reports on performance, correctness, and reliability. The tool supports multiple evaluation modes including unit tests, integration tests, and full system benchmarks. Check the documentation for examples and best practices.

Why should MCP server developers benchmark their tools before shipping?

Most MCP developers are shipping blind, hoping their tools improve agent performance without any rigorous evidence. An MCP server that returns slow or poorly shaped responses can cause agents to loop, hallucinate, or ignore the tool entirely. I ran into this firsthand when my company spent three weeks trying to evaluate our own MCP server using existing coding benchmarks, only to find that every available tool was designed to measure the LLM itself, not the tools it uses. Benchmarking lets you quantify the actual impact of your server on agent success rates and token efficiency rather than relying on gut feeling. That gap is exactly why I built mcpbr: to give MCP developers a way to measure real performance differences with and without their tool in the loop.

What metrics matter most when evaluating an MCP server's performance?

The key metrics to watch are benchmark task success rate (did your MCP actually help the agent solve more problems?), tool adoption rate (how often the agent chooses to call your tool), tool failure rate, and token efficiency (whether your tool saves or wastes context window). Building mcpbr taught me that MCP servers should be tested like APIs, not like plugins. APIs have contracts they are expected to fill, while plugins mostly need to avoid crashing. An MCP server needs to not only return the right size and shape of data, it also needs to fulfill the implicit promise made in its tool description, or agents won't keep reaching for it. These metrics go beyond simple pass/fail and capture whether your server is actually pulling its weight in an agent workflow.

How is mcpbr different from existing coding benchmarks like SWE-bench?

Existing coding benchmarks like SWE-bench are designed to measure the performance of the underlying LLM, not the tools it uses. mcpbr is built specifically to treat MCP servers as first-class evaluation candidates. It runs the same SWE-bench-verified tasks with and without your MCP to isolate its actual impact on agent performance. mcpbr works by spinning up prebuilt Docker containers for each task, injecting a headless Claude Code instance with a special configuration, and capturing detailed logs and traces from every tool call. That gives you the data to debug failed calls, spot patterns in agent behavior, and quantify whether your MCP is helping or just burning tokens. It was designed with a flexible architecture to support other agent harnesses, API providers, and benchmarks in the future.

What is musegpt and what problem does it solve for music producers?

musegpt is a VST3 plugin I developed that brings local LLM inference into Digital Audio Workstations. It solves a problem for music producers who want AI assistance without cloud services, internet connectivity, or sharing sensitive creative work. The plugin runs entirely on your machine using llama.cpp. Your music and prompts never leave your computer. No internet is required once models are downloaded, and it works as a standard VST3 plugin in any compatible DAW with any GGUF format models. I built it with C++ and JUCE. The project is now archived, but it demonstrated the feasibility of running sophisticated AI models directly within creative tools.

What DAWs does musegpt support and how do I install it?

musegpt works with any DAW that supports the VST3 standard, including Ableton Live, FL Studio, Logic Pro, Cubase, Reaper, Studio One, and Bitwig. To install, download the plugin from the releases page, copy the VST3 bundle to your system's VST3 folder, download your preferred GGUF model weights, configure the plugin to point to your model, and scan for new plugins in your DAW. musegpt is currently archived, so active development has paused. The code and builds remain available for anyone interested in local AI inference within music production workflows.

Why did Grey Newell archive the musegpt project?

musegpt successfully validated the core hypothesis that local LLM inference within music production tools is technically feasible and can provide value to creators. The project demonstrated that on-device inference in creative tools works. I archived it because my focus shifted to other research areas, including MCP server evaluation with mcpbr. The broader ecosystem of AI music tools has also grown significantly since I created musegpt, and keeping up with rapid changes in model formats and inference engines requires sustained effort. The project remains valuable as a reference implementation showing how to integrate llama.cpp with JUCE, and as an early exploration of local AI in creative workflows. The code is open source and available for anyone interested in building upon these ideas.

What technical articles has Grey Newell published on the AWS blog?

I've authored several articles published on official AWS blogs. On the AWS Architecture Blog, I wrote about implementing event-driven invoice processing for resilient financial monitoring at scale. The article covers designing serverless systems to process 86 million daily invoice events with near real-time visibility, including cellular architecture patterns and EventBridge routing strategies. On the AWS Training & Certification Blog, I wrote the roadmap for earning all 12 AWS Certifications, sharing the 30-day sprint method and 2357 spaced repetition technique. I also wrote practical exam-taking strategies including the work backwards technique and process of elimination methods.

What press interviews and podcast appearances has Grey Newell been featured in?

I've been featured in interviews and podcasts discussing cloud computing, AI, and technology. In my WhizLabs interview, I shared my complete AWS certification journey covering all 12 certifications, my transition from software development to solutions architecture, and study strategies including the 30-day sprint method. I also discussed common misconceptions about cloud computing and lessons about persistence from applying to Amazon multiple times. On the AI Rebels podcast, I discussed the intersection of AI and startups, Amazon's PR/FAQ framework, AI's impact on the creator economy, and considerations around AI oversight and ethical data sourcing.

Where can I watch or listen to Grey Newell's interviews about AWS certifications and AI?

You can watch my interviews on YouTube. My WhizLabs interview covers the complete path to earning all 12 AWS certifications, study strategies, exam tips, and career advice. The AI Rebels podcast explores my perspectives on AI's future, the creator economy, and product development frameworks like Amazon's PR/FAQ.

Has Grey Newell been featured in any AWS career or culture content?

I was featured on the AWS Careers page in an article about how customer obsession drives innovation at AWS. As one of 15 AWS employees selected, I shared perspectives on working backwards from real customer problems. My quote: "We want to solve problems that are relevant to the real world...working backwards from that feels more like solving real problems." This reflects my approach as an AWS Solutions Architect, where understanding genuine customer needs was always the starting point for designing cloud solutions. The working backwards methodology became a core principle in how I approached both technical architecture and product development.

Grey Newell - Computer Science Researcher

Why I Built mcpbr

2026-02-06

MCP developers are shipping tools without evidence they work. I built mcpbr to find out. Here are results from a 500-task controlled SWE-bench experiment that surprised us.

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