What is the Model Control Protocol and Does OpenAI's ChatGPT Operator implement it?

Yes — OpenAI’s ChatGPT Operator, especially as seen in GPTs with Actions (in the GPT Builder or GPT-4 Turbo environment), essentially implements a lightweight version of an MCP.

What ChatGPT Operator (with Actions) Does

When you define a custom GPT using the GPT Builder and provide Actions (i.e., function calling via external APIs), the GPT:

1. Reads a schema (OpenAPI or JSON schema) describing what each tool/API can do.
2. Chooses a tool based on context and user input.
3. Automatically formats the tool call (arguments, endpoint, parameters).
4. Sends the request to the tool (via the server running the Action).
5. Receives and integrates the result back into its conversational or reasoning flow.

This is LLM-initiated orchestration: the LLM is in control, deciding what to do, how, and when.

How This Aligns with MCP for LLM-Orchestration

MCP Concept	ChatGPT Operator Equivalent
Agent discovery	OpenAPI schema + Action descriptions
Instruction schema	Function call format (JSON-like structured input)
Context tracking	Conversation memory + dynamic tool choice
Event/callback handling	Return value from tool interpreted in reasoning
Chaining tasks	Done implicitly through conversation or prompt flow
Guardrails/safety	Scopes, auth, and content moderation policies

What's Missing (Yet Possible with Extensions)

The current OpenAI system doesn’t yet natively support:

• Asynchronous agent orchestration
• Multi-agent collaboration (e.g., task routing between agents)
• Dynamic tool discovery at runtime
• Custom state memory across multiple actions/tools

These are features that could be layered on top of ChatGPT using:

• External agents or orchestration frameworks (e.g., LangGraph, AutoGen)
• A wrapper API or middle layer that tracks state and manages tool availability

Summary

Yes — OpenAI's ChatGPT Operator is a concrete early implementation of a Model Control Protocol for LLMs. It allows the LLM to act as a controller, making autonomous decisions about which functions (i.e., agents or APIs) to use, and how to use them.

If you're considering building a full-fledged agentic platform or want help creating an extensible MCP layer compatible with GPT, we can help draft that architecture or spec. Want to go there?