Dear DeepLearning.AI Community,
I recently completed the MCP: Build Rich-Content Apps… short course and was really impressed by the developments! I have a question/challenge I’d like to raise to the community:
As the number of MCP Servers/Clients scales and the Registry API is set up for autonomous MCP Server discovery, how do we avoid the same problem MCP aims to solve? Specifically, with so many MCP servers in the Registry API, the context available to the model orchestrating, or deciding which server to use, could become a limiting factor.
Is there a way to develop a standardized orchestration method, essentially, a standard way for the model to choose which MCP server to use in the Registry API, rather than relying purely on inference?
I’m hesitant to think that layering multiple orchestrators is the solution, as there’s always a trade-off between the amount of options an orchestrator can consider (context) and the cumulative probability of errors increasing with each additional layer in the decision chain.
An example solution might be that instead of having the LLM interpret the tools, prompts, and resources available on every server in the Registry API, could we use RAG (or a similar approach) to first retrieve the most relevant servers, and then have the orchestrator choose from that subset?
I think it’s important to consider this before the Registry API is set up, as the solution may require adjustments to how MCP Servers are described and added to the registry. In the simplified RAG example, each MCP Server would need a description when added to the registry, or perhaps a concatenation of the descriptions of all its tools, resources, and prompts, which would then need to be embedded for RAG to work effectively.
I’m still in the early stages of learning about MCP, so I welcome any corrections or insights if I’m missing something.
Thank you!