Overview

ClickHouse MCP is the Model Context Protocol server for ClickHouse, the open-source columnar database built for real-time analytical queries at scale. It enables AI assistants to explore schemas, execute SQL queries, and analyze data across ClickHouse clusters through a standardized MCP interface.

Developed by ClickHouse Inc. with an official implementation maintained on GitHub, this connector turns ClickHouse from a backend analytics engine into an interactive data source for AI-driven analysis. Teams can run ad-hoc queries, explore table structures, and generate insights from billions of rows — all through natural language conversations with their AI assistant.

ClickHouse powers analytics infrastructure at companies like Uber, eBay, and Cloudflare, processing petabytes of data daily. Giving AI assistants direct query access to this data unlocks powerful agentic analytics workflows, but also introduces significant data exposure risks that demand careful governance.

Key Features

Schema Discovery

List databases, tables, and columns with full type information. AI assistants can explore the data model, understand table relationships, and identify relevant datasets before writing queries.

SQL Query Execution

Execute analytical SQL queries against ClickHouse clusters and return structured results. Queries run in read-only mode by default, though write access can be explicitly enabled.

Multiple Transport Support

Connect via STDIO, HTTP, or Server-Sent Events (SSE) transports. Supports TLS encryption and optional JWE-based authentication for secure connections to production clusters.

Automatic Tool Generation

Generate MCP tools automatically from ClickHouse views, enabling teams to expose curated analytical endpoints rather than raw table access as a governed abstraction layer.

Query Result Formatting

Results are returned in structured formats suitable for further AI processing, enabling multi-step analytical workflows where the AI assistant can iterate on queries based on intermediate results.

Capabilities

ClickHouse MCP exposes 4 tools for AI agents.

4 Read

Tool	Operation	Risk	Description
`query` Executes analytical query	Read	Medium Risk	Executes analytical query
`list_databases` Lists available databases	Read	Low Risk	Lists available databases
`describe_table` Shows table schema	Read	Low Risk	Shows table schema
`list_tables` Lists tables in database	Read	Low Risk	Lists tables in database

Use Cases

Strategy-Aligned Use Cases

Self-Service Analytics

AI assistants can translate natural language questions into ClickHouse SQL, enabling non-technical stakeholders to explore analytical data without writing queries. This democratizes access while maintaining a governed query layer.

Real-Time Dashboard Generation

Pull metrics from ClickHouse to generate on-demand reports and visualizations. AI assistants can synthesize data from multiple tables to create executive summaries, KPI snapshots, and trend analyses.

Anomaly Investigation

When monitoring systems detect anomalies, AI assistants can drill into ClickHouse data to investigate root causes, correlate events across dimensions, and generate incident reports with supporting data.

Data Quality Monitoring

AI workflows can run scheduled queries to check for data quality issues, schema drift, or unexpected patterns in analytical pipelines, surfacing problems before they affect downstream reporting.

Considerations

Before You Adopt

ClickHouse databases often contain massive volumes of business-critical data including user behavior, financial transactions, and operational metrics. Unrestricted query access can expose sensitive information across the entire analytical data warehouse.
Poorly constructed queries against large ClickHouse tables can consume significant cluster resources. AI-generated queries may inadvertently trigger full table scans on billion-row tables, impacting production analytics performance.
While read-only mode is the default, some configurations allow write operations including INSERT statements and table modifications. Organizations must enforce strict read-only policies unless write access is explicitly required and governed.
Analytical databases frequently contain PII, financial data, and other sensitive fields alongside general metrics. Column-level access controls should be implemented to prevent AI assistants from accessing protected data categories.
All AI-initiated queries should be logged with full context including the requesting user, the query text, and the result set size. This audit trail is essential for data governance compliance and incident investigation.

Stratafy Fit

Integration Potential

4/5

ClickHouse MCP is a high-value governance target for Stratafy. Analytical databases are among the most sensitive data assets in any organization, containing aggregated business intelligence across customers, revenue, and operations. Stratafy can enforce query-level access policies that restrict which tables and columns AI assistants can access, implement query review workflows for sensitive datasets, and maintain complete audit trails of every AI-initiated analytical query. The combination of massive data volumes and direct SQL access makes governance essential to prevent accidental data exposure or resource abuse.

ClickHouse MCP