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MCP Neo4j Memory Server

@sylweriusz

Repository

Basic information

Category

Memory & Knowledge

License

MIT

Transports

stdio

Author

sylweriusz

About MCP Neo4j Memory Server

No overview available yet

MCP Neo4j Memory Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-neo4j-memory-server": {
      "command": "docker",
      "args": [
        "run",
        "\\"
      ]
    }
  }
}

MCP Neo4j Memory Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Neo4j Memory Server?

An MCP server that gives AI assistants persistent, intelligent memory using Neo4j’s graph database. It stores memories as interconnected knowledge nodes, supports semantic and exact search, creates typed relationships, organizes data by project, and tracks knowledge evolution over time.

How to use MCP Neo4j Memory Server?

Install via npm install @sylweriusz/mcp-neo4j-memory-server, then add the tool to your Claude Desktop config with environment variables NEO4J_URI, NEO4J_USERNAME, and NEO4J_PASSWORD. You must run a Neo4j database with the GDS plugin (recommended: DozerDB). Four unified MCP tools—memory_store, memory_find, memory_modify, and database_switch—handle all memory operations.

Key features of MCP Neo4j Memory Server

  • Stores memories as nodes with typed relationships and temporal metadata
  • Unified search: vectors, exact matches, wildcards, and graph traversal
  • Creates multiple memories with relations in a single batch request
  • Isolates projects via separate databases with instant switching
  • Filters memories by relative or absolute date on any temporal field
  • Zero-fallback error handling for reliable debugging

Use cases of MCP Neo4j Memory Server

  • Remember facts, preferences, and context across AI conversations
  • Find relevant memories using semantic search or exact ID lookups
  • Create meaningful connections between memories with cross-references
  • Organize and isolate memories for different projects or topics
  • Track how knowledge changes over time with versioned observations

Frequently asked questions

1

What database does the server require?

It requires Neo4j with the Graph Data Science (GDS) plugin. The recommended setup is DozerDB (docker pull graphstack/dozerdb). GDS is necessary for vector operations.

2

What are the four MCP tools?

`memory_store` (create memories with relations), `memory_find` (search/retrieve), `memory_modify` (update, delete, manage observations/relations), and `database_switch` (change project context).

3

How do I troubleshoot vector search failures?

Check the server logs for `[VectorSearch] GDS Plugin detected`. If missing, ensure the GDS plugin is installed and Neo4j is running with the correct configuration.

4

Can I use this server without a graph database?

No. Neo4j (with GDS) is a required persistent storage layer. The server has no fallback or in‑memory mode.

5

How do I connect to an existing Neo4j instance?

Set the `NEO4J_URI`, `NEO4J_USERNAME`, and `NEO4J_PASSWORD` environment variables in your MCP client config. Test connectivity via `curl http://localhost:7474`.

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