"Neo4j" matching MCP servers:

A fork of the Neo4j Model Context Protocol server that enables interaction with Neo4j knowledge graphs through environment variable configuration and improved options for remote connections.

Extracts structured operator data from Neo4j query plans and provides rich context for MCP clients to deliver intelligent optimization recommendations.

This read-only MCP Server allows you to connect to Neo4J data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for Neo4J (https://www.cdata.com/drivers/neo4j/download/mcp).

Manages a Neo4j knowledge graph of reusable Python functions, documentation, and environment variables, enabling dynamic script composition and execution with automatic dependency management and secret protection.

Provides a graph-based code analysis to coding agents, enabling tools to trace call dependencies, assess change impact, and identify hotspots using tree-sitter and Neo4j.

Turns any LLM into a coding co-pilot with production-ready MCP server providing lint, test, execution, generation, and graph tools. Features Neo4j persistence, OpenAI Agent integration, REST API, and React frontend for comprehensive code development assistance.

Enables intelligent exploration of Unreal Engine documentation through a Neo4j-based knowledge graph. Supports concept relationship searches, learning path discovery, and dependency tracking between engine features using bilingual Chinese-English queries.

Builds rich code graphs from TypeScript/NestJS codebases using AST analysis and Neo4j, enabling semantic search, natural language querying, and intelligent graph traversal to provide deep contextual understanding of code relationships and dependencies.

Enables querying a Neo4j-based code graph for Python projects, providing tools for code structure, call graph, and test coverage analysis.

Provides persistent memory capabilities for Claude Code using Neo4j graph database to track development tasks, code patterns, solutions, and their relationships across sessions and projects, enabling contextual assistance and pattern recognition.

AI-native code intelligence graph that builds a persistent knowledge graph of your codebase in Neo4j and exposes it to AI assistants via MCP, enabling contextual code analysis, impact analysis, and dependency tracking.

A production-ready MCP server that enables users to interact with Neo4j databases through health checks and Cypher query tools. It features a structured, containerized architecture with built-in support for Azure deployments and environment-driven configuration.