Kuzu MCP Server Overview

The Kuzu MCP Server bridges large language models with graph databases, enabling AI assistants to introspect and manipulate Kuzu databases directly through the Model Context Protocol. By exposing a lightweight set of tools, prompts, and schema‑retrieval capabilities, the server allows developers to build conversational agents that can query complex graph structures without leaving the chat interface. This solves a common pain point: integrating real‑time, schema‑aware data access into LLM workflows while keeping database credentials and query logic isolated from the model.

At its core, the server offers two primary tools. retrieves a complete representation of all node and relationship tables, including their properties, allowing an assistant to understand the database structure before formulating queries. executes arbitrary Cypher statements against the Kuzu instance, returning results that can be immediately presented or used for further reasoning. Complementing these tools is the prompt, which translates natural‑language questions into Cypher code. This prompt empowers users to ask high‑level queries in plain English, while the server handles translation and execution, ensuring that the assistant can operate over graph data without manual query crafting.

The server is designed for seamless integration with existing AI workflows. Developers can launch it via Docker, mounting a local database directory and specifying the target file through environment variables. For more granular control or development purposes, a Node.js implementation is also available, allowing the server to be run directly from source. Additionally, a read‑only mode protects production data by rejecting any mutation queries, making it suitable for analytics or reporting scenarios where write access is unnecessary.

Key advantages of the Kuzu MCP Server include:

• Schema Awareness: Automatic retrieval of graph schema eliminates guesswork and reduces query errors.
• Natural‑Language Query Generation: The Cypher generator translates user intent into executable code, lowering the barrier for non‑technical users.
• Secure Execution: Read‑only mode and Docker isolation ensure that database operations are sandboxed.
• Extensibility: The MCP framework allows easy addition of new tools or prompts, enabling future enhancements such as transaction management or advanced analytics.

Typical use cases span data exploration, automated reporting, and chatbot‑driven decision support. For instance, a business analyst can ask an AI assistant to “Show me the top 5 customers by purchase volume,” and the server will fetch the schema, generate a Cypher query, execute it, and return results—all within the conversational interface. This tight integration turns a static graph database into an interactive, AI‑powered knowledge source, dramatically improving developer productivity and user experience.