MCP Memory Bank

Overview

The Mcp Memory Bank is an MCP (Model Context Protocol) server that provides a persistent, vector‑based memory layer for AI assistants. It solves the common problem of statelessness in conversational agents by allowing a Claude‑style assistant to store, retrieve, and reason over user interactions, documents, or any other text data across sessions. This capability is essential for building assistants that need to remember user preferences, historical context, or domain knowledge without relying on external databases or custom code.

At its core, the server exposes a set of MCP resources that let clients index arbitrary text and query it using semantic similarity. Behind the scenes it leverages a ChromaDB vector store coupled with an embedding model (defaulting to Xenova/all-MiniLM-L6-v2) to convert text into dense vectors. When a user asks the assistant a question, the server returns the most relevant passages from its memory bank, enabling the AI to generate responses that reference past interactions or domain‑specific information. This semantic retrieval is far more powerful than keyword matching, as it captures meaning and context.

Key features include:

• Persistent storage: All indexed data lives in a ChromaDB instance that is bundled with the server and persists across restarts via Docker volumes.
• Embeddable API: Clients can add or delete documents through simple HTTP endpoints, making integration into existing workflows trivial.
• Scalable vector search: ChromaDB’s efficient nearest‑neighbor search ensures fast retrieval even with large knowledge bases.
• Configurable embedding model: Developers can swap the default model for any Hugging Face compatible encoder by changing an environment variable, allowing trade‑offs between accuracy and performance.
• Docker‑ready deployment: A single file brings up the application and its database, simplifying local development and production rollouts.

Typical use cases span a wide range of scenarios. A customer‑support bot can remember past tickets and personalize follow‑ups; a research assistant can store abstracts and retrieve them when drafting papers; a personal productivity AI can keep track of user goals, deadlines, and notes. In each case the memory bank acts as a shared knowledge base that the assistant can query in real time, eliminating the need for custom state‑management logic.

Integrating the MCP Memory Bank into an AI workflow is straightforward. An assistant first loads its MCP client, then uses the endpoint to access the memory bank. Whenever a conversation occurs, relevant text fragments are indexed; subsequent queries route through the same MCP channel, allowing the assistant to fetch contextually relevant snippets. Because the server follows the standard MCP protocol, any compliant AI client—Claude, GPT‑like models, or custom agents—can leverage it without modification. This plug‑and‑play nature, combined with the semantic search capability, gives developers a powerful tool to build more coherent, contextually aware AI assistants.