Model Context Protocol FAQs

Question 1

What storage backends does MCP support?

Accepted Answer

MCP supports various storage backends for managing conversation context, including in-memory storage, Redis, and SQLite. This provides flexibility in choosing the right storage solution for your needs.

Question 2

What are the system requirements for running MCP?

Accepted Answer

MCP requires Python 3.8 or higher, a CUDA-supported GPU (recommended), and optionally Docker & Docker Compose for containerized deployment.

Question 3

Which LLM models are supported by MCP?

Accepted Answer

Currently, MCP supports DeepSeek and Llama models. The architecture is designed to be extensible, allowing for the addition of other LLMs in the future.

Question 4

How do I add a new LLM model to MCP?

Accepted Answer

To add a new model, you need to create a new model class file in the `app/models` directory, inherit from the `BaseModel` class, register the model in `app/models/router.py`, and update the routing configurations in `app/core/config.py`.

Question 5

What is Model Context Protocol (MCP)?

Accepted Answer

MCP is a tool that simplifies the management of multiple Large Language Models (LLMs) by providing a unified and standardized API interface. It allows you to easily switch between models like DeepSeek and Llama and manage conversation context effectively.

Model Context Protocol

概要

主な機能

ユースケース