No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by neo4j-contrib · MCP Server · ★ 980
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-neo4j safe to install? View the security audit →
Neo4j Labs MCP Servers Neo4j Labs These MCP servers are a part of the Neo4j Labs program. They are developed and maintained by the Neo4j Field GenAI team and welcome contributions from the larger developer community. These servers are frequently updated with new and experimental features, but are not supported by the Neo4j product team. They are actively developed and maintained, but we don’t provide any SLAs or guarantees around backwards compatibility and deprecation. If you are looking for the official product Neo4j MCP server please find it here. Overview Model Context Protocol (MCP) is a standardized protocol for managing context between large language models (LLMs) and external systems.
| Stars | 980 |
| Forks | 258 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.9470809212251/100 |
| Open Issues | 25 |
| Last Updated | 2026-09-09 |
| Created | 2024-12-03 |
| Platforms | mcp, python |
| Est. Tokens | ~14k |
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mcp-neo4j is Neo4j Labs Model Context Protocol servers. It is categorized as a MCP Server with 980 GitHub stars.
mcp-neo4j is primarily written in Python. It covers topics such as database, mcp, mcp-server.
You can find installation instructions and usage details in the mcp-neo4j GitHub repository at github.com/neo4j-contrib/mcp-neo4j. The project has 980 stars and 258 forks, indicating an active community.
mcp-neo4j is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-neo4j on Agent Skills Hub include excel-mcp-server, dbhub, mysql_mcp_server. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: