# neo4j

> Use this tool when you need to efficiently store and query complex, connected data, solving problems such as data integration, recommendation engines, and network analysis. Neo4j takes in structured and semi-structured data as input and outputs querying results, providing a graph database interface. It is particularly useful in contexts where relationships between data entities are crucial, such as social networks, knowledge graphs, and real-time recommendation systems.

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

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/mikeisfree/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": "mikeisfree-neo4j"
    }
  }
}
```

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