Memgraph MCP Server – a lightweight Python implementation of the Model Context Protocol that exposes Memgraph graph-database operations (run_query, get_schema) to LLM clients such as Claude.
https://github.com/memgraph/mcp-memgraphStop copy-pasting Cypher queries and database schemas into chat windows. The Memgraph MCP Server creates a direct bridge between your graph database and LLMs like Claude, letting you query and explore your data through natural conversation.
If you're working with graph databases, you know the friction: switching between your database tool, documentation, and AI assistant to understand relationships, write queries, and analyze results. You end up manually copying schemas, reformatting query results, and losing context between tools.
Traditional approaches force you to:
This MCP server eliminates that friction by giving Claude (and other MCP-compatible LLMs) direct access to your Memgraph instance. Your AI assistant can now run queries, inspect schemas, and reason about your graph data without you lifting a finger.
Two simple capabilities, massive productivity gain:
The result? Fluid conversations about your data where the AI can explore, query, and analyze your graph database as naturally as discussing any other topic.
Data Exploration: "Show me all users connected to this fraud pattern" → Claude runs the query, analyzes results, and suggests follow-up investigations
Query Development: "Help me find the shortest path between these two entities" → Claude writes the Cypher, executes it, and explains the results
Schema Analysis: "What's the relationship structure around User nodes?" → Claude pulls the schema and explains your data model with actual examples
Performance Debugging: "Why is this query slow?" → Claude examines your query, checks the schema, and suggests optimizations based on your actual data structure
Data Quality Audits: "Find inconsistencies in my product catalog" → Claude explores your data systematically, identifying patterns and anomalies
Setup takes minutes, not hours. Install dependencies, configure your Memgraph connection, add the server to Claude's config, and start querying. No complex authentication flows, no API keys to manage - just direct database access through the MCP protocol.
The server runs locally, keeping your data secure while providing the conversational database access you've been wanting. Your graph database becomes as accessible to your AI assistant as any other tool in your development workflow.
Perfect for developers building knowledge graphs, analyzing social networks, working with recommendation engines, or exploring any connected data where relationships matter more than rigid schemas.
Note: This repository has been merged into the Memgraph AI Toolkit for ongoing development. Head there for the latest updates and to contribute.