MeMCP FAQs

Question 1

Can MeMCP manage the quality of stored information?

Accepted Answer

Yes, MeMCP features a multi-dimensional quality assessment system that scores facts based on novelty, generalizability, specificity, validation, and impact. This system allows for automatic fact classification, prioritization, and quality-based cleanup policies.

Question 2

What kind of search capabilities does MeMCP offer?

Accepted Answer

MeMCP provides advanced semantic search using TF-IDF based indexing and cosine similarity scoring. This allows LLMs to intelligently retrieve relevant facts and insights from their accumulated memory, improving the quality and context of their responses.

Question 3

How does MeMCP integrate with existing LLM workflows?

Accepted Answer

MeMCP is designed for seamless integration via the Model Context Protocol (MCP). It provides a comprehensive set of MCP tools for operations like storing, querying, updating, and managing facts, making it easy for AI assistants and LLMs to interact with its memory system.

Question 4

How does MeMCP provide persistent memory to LLMs?

Accepted Answer

MeMCP stores facts and insights permanently across sessions using robust JSON-based storage with atomic write operations. It automatically loads historical knowledge on startup, preventing data loss and ensuring continuous access to accumulated information.

Question 5

What is MeMCP and what problem does it solve?

Accepted Answer

MeMCP (Memory-Enhanced Model Context Protocol) is a persistent memory system for Large Language Models (LLMs). It solves the problem of stateless LLM interactions by enabling continuous learning and knowledge retention across sessions, allowing LLMs to build cumulative knowledge over time.

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