The question in our industry is no longer whether LLMs can work with market data – it’s how to give them access that is reliable, fast and production-grade. Here’s what that actually looks like when you bring the PrivateMind development environment together with AtlasFeed.
A turnkey development environment
The first thing we keep coming back to is that PrivateMind AI Workspaces simply work straight out of the box. A workspace is a compute environment with OpenCode, Jupyter, a terminal and VS Code built in, along with the standard Python and ML/PyTorch stack and Git integration. Each tool opens as its own browser tab, but it’s one fully integrated system – not four separate apps that you have to wire together yourself.
The experience is genuinely quick and efficient too. A script to pull real-time and historical market data was written in seconds, even with OpenCode configured to hit GLM rather than one of the flagship models. That resonates with our general view that the harness – the agent loop, the tooling, the context management is the defining innovation of 2026. The models themselves are already more than fit for purpose. (Recently the PrivateMind team added Meta Muse to the list of available models – the first heavyweight open-weight US model from Meta – widening the options further.)
400+ data feeds, out of the box
The latest release ships with the AtlasFeed SDK built in as standard, plus API access. In our own test, OpenCode was able to read the SDK documentation and write a simple script to pull both real-time and historical data – and got it right first time.
Every workspace also comes with sample API credentials for delayed CTA data, so you can start pulling feeds right away; add your own AtlasFeed API credentials when you are ready for the full, live set. In other words, the API functionality is available out of the vox for every Options Technology AtlasFeed client, bringing over 400 separate global data feeds, including historical data, directly into the development environment.
The MCP debate, with nuance
There has been a lot of noise lately about MCP for market data, so it’s worth setting out how we see the two working together.
MCP is a great tool for discovering data: requesting snapshots, pulling news and historical data, and answering ad-hoc research and analysis questions. Our AtlasFeed MCP supports all of that, and it does the job well for that use case.
The AtlasFeed API is built for production: it’s feature-rich, fast, and it’s how our clients have been consuming market data for years. It’s the workhorse for real-time streaming and high-volume workloads, and in practice, the pattern that works best is a skills file that lets the LLM hit the API directly, if it even needs to.
In short: MCP for discovery and ad-hoc questions, the API for streaming and scale. Two strong options, each doing what it’s built for, and both available from the same environment.
Where this goes next
The direction is clear: trusted market data, real development tooling, and agents that can act on both, all in one environment. That’s the workflow we’re building, and we’ll keep you posted as it evolves.
If you’re an AtlasFeed client, the SDK and API access are already in the latest PrivateMind release – all you need is your credentials.