KurrentDB MCP Server

Overview

KurrentDB MCP Server bridges the gap between an AI assistant and a high‑performance event stream database. By exposing stream operations as first‑class tool calls, it lets developers query, write, and manipulate event streams without leaving the conversational context. The server solves a common pain point for data‑centric AI workflows: the need to retrieve real‑time or historical event data, build projections, and validate analytics pipelines—all through simple, declarative prompts.

At its core, the server offers eight tool calls that cover every stage of an event‑driven workflow. Developers can read events from any stream, write new events, and even test or deploy projections that transform raw streams into analytic views. The tool lets users draft a projection definition, while and handle deployment. The tool evaluates a projection against sample data, and reports on running projections. Together, these tools provide a complete lifecycle for working with event streams directly from the AI interface.

The value to developers lies in the elimination of boilerplate code and context switching. Instead of writing custom scripts or interacting with a REST API, an AI assistant can invoke to pull the latest 10 events or to inject a new transaction. Projections can be iterated on in real time: draft, test, and deploy—all within the same conversation. This speeds prototyping of analytics dashboards, real‑time monitoring alerts, and data quality checks.

Real‑world scenarios that benefit from KurrentDB MCP Server include building live dashboards for e‑commerce order flows, monitoring user activity streams for anomaly detection, and validating data pipelines before they hit production. In each case, the AI assistant can ask for a snapshot of recent events, suggest projection schemas, or confirm that a new event type is correctly ingested. The server’s access control via the KurrentDB connection string ensures that only authorized clients can read or modify streams, making it suitable for production environments.

Integration with AI workflows is straightforward: any MCP‑compliant client—Claude Desktop, VS Code, Cursor, or Windsurf—can be configured to launch the server as a stdio process. Once connected, the assistant can treat stream operations like natural language actions, enabling rapid iteration and collaborative debugging. The server’s design prioritizes simplicity and completeness, giving developers a single point of entry to all stream‑related tasks while keeping the underlying complexity hidden.