mcp-neo4j is a suite of MCP servers designed to seamlessly integrate Neo4j graph databases with AI models. It empowers developers to use natural language via MCP clients like Claude Desktop to interact with Neo4j and Aura accounts. The suite includes servers for natural language to Cypher query translation (mcp-neo4j-cypher), knowledge graph memory storage (mcp-neo4j-memory), and Neo4j Aura cloud service management (mcp-neo4j-cloud-aura-api).

These servers enable AI models to understand and manipulate graph data, manage cloud instances, and store persistent knowledge. By leveraging mcp-neo4j, developers can build AI-powered applications that utilize the power of graph databases without complex coding. The solution uses Python and integrates via standard input/output or HTTP/SSE, offering flexibility in deployment and usage. This simplifies the process of connecting AI with graph data, unlocking new possibilities for intelligent applications.

NL to Cypher Translation

The mcp-neo4j-cypher server translates natural language queries into Cypher, Neo4j's query language, enabling users to interact with graph databases using intuitive language. It retrieves the database schema for a configured database and uses this information to generate Cypher queries that can read and write data. This allows users to ask questions or request actions in plain English, which are then converted into executable database commands. For example, a user could ask "What are the top 5 most frequently sold products?" and the server would translate this into a Cypher query that retrieves the relevant data from the Neo4j database. This server simplifies database interaction, making it accessible to users without Cypher expertise.

Technically, this server likely uses a combination of natural language processing (NLP) techniques and the database schema to construct valid Cypher queries. The generated queries are then executed against the Neo4j database, and the results are returned to the user.

Knowledge Graph Memory

The mcp-neo4j-memory server provides a knowledge graph memory function, allowing AI models to store and retrieve entities and relationships within a Neo4j database. This enables persistent storage of information across different sessions, conversations, and clients. The server acts as a personal knowledge graph, retaining facts and connections established during interactions. For instance, if a user tells the AI "I worked on the Neo4j MCP Servers today with Andreas and Oskar," this information can be stored as nodes and relationships in the Neo4j graph. Later, the user can ask "Who did I work with on the Neo4j MCP Servers?" and the server will retrieve the stored information from the graph.

This server enhances the AI model's ability to remember and reason about past interactions, creating a more personalized and context-aware experience. The underlying implementation involves storing entities as nodes and relationships as edges in the Neo4j graph database.

Aura Cloud Management

The mcp-neo4j-cloud-aura-api server enables the management of Neo4j Aura instances directly from an AI assistant chat. Users can create, destroy, find, and scale Aura instances, as well as enable features, all through natural language commands. This eliminates the need to use the Neo4j Aura console or API directly, streamlining the management process. For example, a user could say "Create a new instance named mcp-test for Aura Professional with 4GB and Graph Data Science enabled," and the server would handle the creation of the instance via the Neo4j Aura API.

This server simplifies cloud database management, making it more accessible and convenient for users. The server likely interacts with the Neo4j Aura API using appropriate authentication and authorization mechanisms to perform the requested actions.

Integration Advantages

The mcp-neo4j suite offers significant integration advantages within the MCP ecosystem. By providing specialized servers for Cypher translation, knowledge graph memory, and cloud management, it allows AI models to seamlessly interact with Neo4j databases and Aura cloud services. This tight integration enables AI applications to leverage the power of graph databases for knowledge representation, reasoning, and data management. The use of MCP as a standardized protocol ensures interoperability with various AI clients and servers, fostering a flexible and extensible ecosystem. This allows developers to easily incorporate Neo4j functionality into their AI workflows, creating more powerful and intelligent applications.