You don't need an OpenAI sub — MCP is provider-agnostic at the protocol layer, that's the whole point of it. The Robinhood MCP endpoint https://agent.robinhood.com/mcp/trading is just an HTTP-streamed MCP server (probably JSON-RPC over SSE). Any client that speaks MCP can hit it.

Concrete working stacks for this:

• Claude Desktop / Claude Code: drop the server in claude_desktop_config.json (or .mcp.json for Code) under mcpServers. Works out of the box. Robinhood will hand you an OAuth or API-key flow on first connect.
• Local model via mcp-cli or fast-agent: pip install fast-agent-mcp, point it at any OpenAI-compatible endpoint — that includes Ollama, llama.cpp's server, vLLM, LM Studio, or ppq.ai. So you can drive the Robinhood MCP with a fully local gpt-oss:20b/120b if you want to. ppq.ai works fine here because MCP doesn't care which model is on the other end; the client does the tool routing.
• n8n / OpenAgents / Cline / Continue.dev: all have MCP client support now. Continue is probably the lowest-friction GUI for trying it.

The one thing I'd flag: an "agentic trading" MCP plus a frontier model is roughly the most dangerous tool/model combo in the current surface — there is no undo on a market order, and prompt injection from anything in the model's context (a news headline, a tweet, a quoted email) can be coerced into "sell everything". If you actually wire this up, sandbox the position size at the broker side (Robinhood-side limit) before you sandbox it at the agent side, because the agent-side limit is one jailbreak away from being ignored. Run it paper-money first for at least a few weeks.

(Re: the portfolio — fully agentic execution of a published portfolio is fine; the interesting research question is whether the agent should be allowed to deviate from it based on news context, which is where most of the alpha and all of the risk lives.)