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DimensionNeo4j Data Modeling MCP ServerCreate, validate, and visualize Neo4j graph data modelsMCP Toolbox for DatabasesGoogle's official database MCP toolbox — define AI-accessible database tools via config
FMRS51 / 100 · D74 / 100 · B
Reliability8 / 2013 / 20
Security and permissions10 / 2013 / 20
Maintenance13 / 2017 / 20
Documentation9 / 2016 / 20
Setup experience11 / 2015 / 20
Best for
  • Neo4j users who want to design and validate graph models before committing them
  • Developers doing graph data modeling from MCP-compatible clients
  • Teams that want precise control over which database operations an AI can perform, rather than open arbitrary SQL execution
  • Scenarios needing a unified MCP setup across multiple database engines
Not for
  • Teams requiring official Neo4j product support or SLAs
  • Production environments requiring backwards-compatibility guarantees
  • Lightweight cases that just want to run a few ad-hoc SQL queries without maintaining a tools.yaml config (a simpler single-database MCP may be a better fit)
Required permissions
  • Runs locally over stdio using local machine resources
  • Database credentials (username/password/connection string) are supplied via env vars or config
  • A tool's actual permission is whatever SQL statement is defined in tools.yaml — designed for least privilege, but misconfiguration can still over-expose access
Risks and side effects
  • Neo4j Labs project with frequently updated experimental features and no guarantees around backwards compatibility or deprecation
  • Not supported by the Neo4j product team
  • Importing/exporting models from Arrows.app may send model data to an external service
  • If tools.yaml defines SQL statements that allow unconstrained writes or deletes, the AI could accidentally modify data
  • The prebuilt toolsets (--prebuilt) favor convenience and may expose broader query capability than a specific business actually needs — use a custom tools.yaml in production
Supported clientsClaude Code, Gemini CLI, Zed, Antigravity
Tools00