Kurrent unveils open-source MCP Server for AI-driven databases

Kurrent has released its open-source MCP Server for KurrentDB, enabling developers to interact with data in the KurrentDB database using natural language and AI agents rather than traditional coding methods.

The Kurrent MCP Server offers new functionalities, allowing developers not only to query data but also to create, test, and debug projections directly through conversational commands. This feature is not available in other MCP server implementations, establishing a novel approach to database interaction by integrating AI-driven workflows into the database layer.

Central to this release is the introduction of a self-correcting engine, which assists in automatically identifying and fixing logic errors during the prototyping phase. This reduces the need for manual debugging loops, streamlining the development process significantly for users building or modifying projections.

The software is fully open-source and released under the MIT license, with documentation and a development roadmap available on GitHub. This permits both enterprise users and open-source contributors to adopt, customise, and improve the KurrentDB MCP Server without licensing restrictions.

Kurrent MCP Server supports natural language prompts for tasks such as reading streams, listing streams within the database, building and updating projections, writing events to streams, and retrieving projection status for debugging. These capabilities aim to make the visual and analytical exploration of data more accessible and conversational for users with varying levels of technical expertise.

The MCP Server is compatible with a broad range of frontier AI models, such as Claude, GPT-4, and Gemini. It can be integrated with popular IDEs and agent frameworks, including Cursor and Windsurf. This compatibility enables developers to leverage their preferred tools while reducing friction points typically associated with traditional database interactions.

Addressing the new approach, Kirk Dunn, CEO of Kurrent, said, "Our new MCP Server makes it possible to use the main features of the KurrentDB database, like reading and writing events to streams and using projections, in a way that's as simple as having a conversation. The system's ability to test and fix itself reduces the need for debugging and increases reliability. Copilots and AI assistants become productive database partners rather than just code generators, seamlessly interfacing with KurrentDB."

The server's key functions are designed to reduce development times for database tasks, enabling a focus on higher-value project work. Eight core capabilities are available, including Read_stream, List_streams, Build_projection, Create_projection, Update_projection, Test_projection, Write_events_to_stream, and Get_projections_status. Each of these responds directly to natural language instructions provided by the developer or AI agent.

Kurrent has highlighted opportunities for the open source community to participate in the MCP Server's ongoing development. Developers can contribute code, report or tackle issues, and suggest new features through the project's GitHub repository and discussion forums. Comprehensive educational resources and installation guides are intended to help developers quickly integrate the MCP Server with KurrentDB for various use cases.

Lokhesh Ujhoodha, Lead Architect at Kurrent, commented, "Before, database interactions required developers to master complex query languages, understand intricate data structures, and spend significant time debugging projections and data flows. Now, everything agentic can interface with KurrentDB through this MCP Server. We're not just connecting to today's AI tools, but we're positioning for a future where AI agents autonomously manage data workflows, make analytical decisions and create business insights with minimal human intervention."

Kurrent emphasises that its MCP Server aims to remove barriers historically associated with database development by supporting conversational, agent-driven workflows. This aligns with broader trends towards AI-native infrastructure in enterprise environments, where human and algorithmic agents increasingly collaborate to deliver data-driven business outcomes.