MCP guides
Model Context Protocol (MCP) is a standard developed by Anthropic that enables AI assistants to seamlessly integrate with external systems. This protocol allows AI assistants to connect to data sources, APIs, databases, and more in a secure and standardized way.
MCP creates a universal interface between AI models and various services, eliminating the need for custom tool implementations for each integration. You can think of it as a universal API standard designed specifically for AI systems.
The key advantage of MCP is that AI libraries only need to implement support for the protocol once. After that, all MCP-compatible services become immediately accessible, saving AI library maintainers a lot of time.
What is MCP's architecture?
MCP follows a client-server architecture:
- Clients (like Claude Desktop, Cursor, or VS Code) establish connections with MCP servers. You can see a collection of clients in the awesome-mcp-clients GitHub repository.
- Servers expose tools and capabilities through standardized interfaces. You can see a collection of servers in the awesome-mcp-servers GitHub repository.
- AI models can then use these tools to access external data and functionality when needed
A diagram showing the architecture is below:
Does ClickHouse have an MCP Server?
It does! The ClickHouse MCP Server offers the following tools:
run_select_query- Execute SQL queries on your ClickHouse cluster.list_databases- List all databases on your ClickHouse cluster.list_tables- List all tables in a database.
Guides for using the ClickHouse MCP Server
Below are some guides showing how to use the ClickHouse MCP Server.
| Page | Description |
|---|---|
| Enabling the ClickHouse Cloud Remote MCP Server | This guide explains how to enable and use the ClickHouse Cloud Remote MCP |
| How to build a ClickHouse-backed AI Agent with Streamlit | Learn how to build a web-based AI Agent with Streamlit and the ClickHouse MCP Server |
| How to build a LangChain/LangGraph AI agent using ClickHouse MCP Server. | Learn how to build a LangChain/LangGraph AI agent that can interact with ClickHouse's SQL playground using ClickHouse's MCP Server. |
| How to build a LlamaIndex AI agent using ClickHouse MCP Server. | Learn how to build a LlamaIndex AI agent that can interact with ClickHouse MCP Server. |
| How to build a PydanticAI agent using ClickHouse MCP Server. | Learn how to build a PydanticAI agent that can interact with ClickHouse MCP Server. |
| How to build a SlackBot agent using ClickHouse MCP Server. | Learn how to build a SlackBot agent that can interact with ClickHouse MCP Server. |
| How to build an AI Agent with Agno and the ClickHouse MCP Server | Learn how build an AI Agent with Agno and the ClickHouse MCP Server |
| How to build an AI Agent with Chainlit and the ClickHouse MCP Server | Learn how to use Chainlit to build LLM-based chat apps together with the ClickHouse MCP Server |
| How to build an AI Agent with CopilotKit and the ClickHouse MCP Server | Learn how to build an agentic application using data stored in ClickHouse with ClickHouse MCP and CopilotKit |
| How to build an AI Agent with DSPy and the ClickHouse MCP Server | Learn how to build an AI agent with DSPy and the ClickHouse MCP Server |
| How to build an OpenAI agent using ClickHouse MCP Server. | Learn how to build an OpenAI agent that can interact with ClickHouse MCP Server. |
| Set Up ClickHouse MCP Server with Claude Desktop | This guide explains how to set up Claude Desktop with a ClickHouse MCP server. |
| Set Up ClickHouse MCP Server with LibreChat and ClickHouse Cloud | This guide explains how to set up LibreChat with a ClickHouse MCP server using Docker. |
| Set Up ClickHouse MCP Server with Ollama | This guide explains how to set up Ollama with a ClickHouse MCP server. |
