Clickhouse MCP Server

The ClickHouse MCP Server bridges the gap between large language models (LLMs) such as Claude and high‑performance analytical databases. By exposing ClickHouse table schemas, sample data, and a natural‑language query tool through the Model Context Protocol (MCP), developers can empower AI assistants to ask questions, generate insights, and even write SQL on the fly—all without leaving their preferred chat interface. This eliminates the need for manual query construction, reduces context‑switching, and accelerates data exploration in analytics workflows.

At its core, the server offers a read‑only interface to ClickHouse. It exposes resources that let an LLM retrieve metadata () and fetch table schemas or a handful of sample rows. The tools layer provides two commands: for running vetted SQL statements and , which translates conversational prompts into executable queries. Because the server validates every request, it prevents any destructive DDL or DML operations, ensuring that sensitive data remains protected while still allowing rich analytical interaction.

Developers who work with Claude Desktop on macOS find the integration straightforward. By adding a single entry to the desktop’s configuration file, the ClickHouse MCP Server becomes an instantly available tool. Once connected, a user can ask questions like “What are the top 10 customers by revenue?” and receive a concise answer generated from real query results, or request a visual chart of monthly sales trends—all powered by the underlying ClickHouse engine. The server’s read‑only stance also makes it ideal for environments where data governance is strict, yet exploratory analysis is required.

Real‑world use cases include business intelligence teams that need to prototype dashboards, data scientists exploring new datasets without writing SQL, and support engineers troubleshooting anomalies in production metrics. By enabling natural‑language interactions with ClickHouse, the server turns a complex analytical database into an accessible knowledge base that can be queried conversationally by anyone on the team.

Unique advantages of this MCP implementation are its tight security controls and seamless macOS desktop integration. The server’s design ensures that only safe, read‑only queries are executed, while the lightweight Node.js runtime keeps resource consumption minimal. For developers already using Claude Desktop, adding ClickHouse as a data source becomes a one‑click affair—turning any conversational AI session into an interactive analytics experience.