MCP Memory Graph Server

Overview

The MCP Memory Graph Server provides a durable, MongoDB‑backed persistence layer for knowledge graphs that can be accessed by AI assistants through the Model Context Protocol. It solves a common pain point for developers building conversational agents that need to remember complex relationships over time: storing, updating, and querying entities in a graph format without having to manage the underlying database infrastructure. By exposing CRUD operations, relationship handling, and real‑time update notifications as MCP endpoints, the server lets AI agents treat graph data as first‑class context that can be queried on demand or streamed back to the user in natural language.

Developers gain a flexible, schema‑agnostic storage system that can scale from local prototypes to cloud deployments on MongoDB Atlas. The server automatically translates MCP graph commands into MongoDB operations, allowing agents to create nodes, attach properties, and link entities with directed relationships. Because the graph is stored in a document database, updates are atomic and can be performed concurrently without locking the entire structure. This is especially valuable for multi‑agent or multi‑user scenarios where several assistants may read and write to the same knowledge base simultaneously.

Key capabilities include:

• Entity Management – Create, read, update, and delete nodes with arbitrary key/value pairs.
• Relationship Handling – Define directed edges between entities, optionally enriched with properties such as timestamps or weights.
• Graph Querying – Execute traversal queries to discover related entities, supporting depth‑first or breadth‑first searches.
• Real‑time Updates – Subscribe to change streams so that an AI assistant can react instantly when new facts are added or existing ones modified.
• MongoDB Persistence – Leverage MongoDB’s scalability, backup, and security features while keeping the API simple for MCP clients.

Typical use cases span from personal knowledge bases (e.g., remembering user preferences or past interactions) to enterprise knowledge graphs that integrate product catalogs, customer data, and support tickets. An AI assistant can ask the graph “What products does this user own?” or “Show me all teammates who worked on project X,” and receive structured answers that are automatically persisted for future conversations. In research settings, the server can store experimental results and their provenance, allowing agents to reference prior findings during literature reviews or hypothesis generation.

Integrating the Memory Graph Server into an MCP workflow is straightforward: add a “Memory” server entry in your MCP configuration, supply the MongoDB connection string via an environment variable, and start issuing graph commands from your assistant. Because the server speaks MCP natively, no custom adapters are needed; developers can focus on modeling their domain and writing prompts that leverage graph queries. The combination of persistent storage, real‑time change notification, and a clean MCP interface makes this server an essential component for any AI system that requires reliable, relational context over time.