# Neo4j

> Use this tool when you need to interact with graph databases using natural language interfaces, solving problems such as complex data querying and entity relationship management. It provides inputs through conversational interfaces like Claude Desktop and outputs knowledge graph data, enabling users to build persistent memory structures. Ideal for use cases requiring intuitive access to graph data, such as querying and managing entities and relationships.

Canonical page: https://skillsregistry.net/skills/guanxinyuan-neo4j  
JSON: https://api.skillsregistry.net/v1/skills/guanxinyuan-neo4j

## Description

Neo4j MCP Servers provide natural language interfaces to Neo4j graph databases through three specialized components: mcp-neo4j-cypher for executing Cypher queries, mcp-neo4j-memory for storing knowledge graph data in Neo4j, and mcp-json-memory as a file-based reference implementation. The project uses TypeScript for the memory servers and Python for the Cypher server, with a shared graphrag-memory library defining the knowledge graph interface. These servers enable users to query graph data, manage entities and relationships, and build persistent memory structures through Claude Desktop or any MCP client, making complex graph operations accessible through conversational interfaces.

## Trust

- **Trust score (0–1):** 0.92
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/guanxinyuan-neo4j)
- **Repository:** <https://github.com/guanxinyuan/neo4j>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "guanxinyuan-neo4j"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/guanxinyuan-neo4j` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/guanxinyuan-neo4j/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt