Neo4j MCP server
Project description
Neo4j MCP
Neo4j MCP gives AI assistants and LLM-powered tools direct, structured access to your Neo4j graph database. By implementing the Model Context Protocol (MCP), it acts as a bridge between any MCP-compatible client, such as Claude, Cursor, or VS Code with MCP support, and your Neo4j instance.
Features
- Explore your graph schema - discover node labels, relationship types, and property keys
- Let AI reason on your data model without prior knowledge
- Run Cypher queries - execute, read, and write queries against your database in response to natural language prompts
- Inspect and analyze data - retrieve nodes, relationships, and paths to answer questions, generate summaries, or feed data to other workflows
Tools
get-schema— introspect labels, relationship types, property keysread-cypher— execute read-only Cypher queries that do not modify database data, enforced viaEXPLAINand Neo4j's query-type classification. Note: custom procedures or functions incorrectly classified as read-only by Neo4j may bypass this check; ensuring correct classification is the responsibility of the procedure/function maintainer.write-cypher— execute write Cypher queries (disabled ifNEO4J_READ_ONLY=true)list-gds-procedures— list available GDS procedures
Installation
Install with PyPI:
pip install neo4j-mcp-server
Otherwise see MCP documentation -> Installation.
Server configuration (VSCode)
Create / edit mcp.json:
{
"servers": {
"neo4j": {
"type": "stdio",
"command": "python",
"args": ["-m", "neo4j_mcp_server"],
"env": {
"NEO4J_URI": "bolt://localhost:7687",
"NEO4J_USERNAME": "neo4j",
"NEO4J_PASSWORD": "password",
"NEO4J_DATABASE": "neo4j",
"NEO4J_READ_ONLY": "true",
"NEO4J_TELEMETRY": "false",
"NEO4J_LOG_LEVEL": "info",
"NEO4J_LOG_FORMAT": "text",
"NEO4J_SCHEMA_SAMPLE_SIZE": "100"
}
}
}
}
See MCP documentation > Configuration for more details.
Links
- Documentation: The official Neo4j MCP documentation.
- Discord: The Neo4j discord channel.
- Contributing Guide: Contribution workflow, development environment, mocks and testing.
For issues and feedback, you can also create a GitHub issue with reproduction details (omit sensitive data).
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