Overview of the MCP Memory Server

The MCP Memory Server provides a lightweight, persistent knowledge‑graph backend that lets Claude remember user‑specific facts across sessions. By exposing a set of MCP tools, it turns ordinary chat interactions into a continuously evolving memory store: entities such as people or organizations can be created, linked by relations, and enriched with atomic observations. This capability is essential for building assistants that maintain context over time without relying on stateless prompt engineering.

The server’s core data model is intentionally simple yet expressive. An entity represents a concept with a unique name, a type label (e.g., person, organization), and a list of observations. Relations are directed edges that describe how two entities interact, always expressed in active voice (e.g., works_at). Observations are single‑fact strings attached to entities, allowing fine‑grained updates and deletions. Together these components form a directed graph that can be queried, modified, or inspected in full.

Key MCP tools make the graph a first‑class citizen for developers. create_entities and create_relations allow bulk addition, while add_observations lets you append new facts to existing nodes. Deletion tools—delete_entities, delete_observations, and delete_relations—provide safe, idempotent removal with cascading behavior. Retrieval is facilitated by read_graph, which returns the entire structure, and search_nodes/ open_nodes, which support targeted queries by name or content. Because all tools are stateless RPC calls, they integrate seamlessly into any MCP‑enabled workflow, whether the assistant is running locally or in a cloud environment.

In practice, this server enables use cases such as personalized scheduling assistants that remember user preferences, knowledge‑base agents that accumulate domain facts over time, or multi‑turn dialogue systems that can reference past interactions without re‑prompting. Developers benefit from having a durable, queryable memory that scales with conversation length and can be backed by any persistence layer the underlying implementation supports. The MCP Memory Server’s straightforward API, combined with its graph‑based model, gives AI assistants a robust foundation for long‑term context and knowledge accumulation.