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This guide explores how to combine Chainlit’s powerful chat interface framework with the ClickHouse Model Context Protocol (MCP) Server to create interactive data applications. Chainlit enables you to build conversational interfaces for AI applications with minimal code, while the ClickHouse MCP server provides seamless integration with ClickHouse’s high-performance columnar database.

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Prerequisites

  • You’ll need an Anthropic API key
  • You’ll need to have uv installed

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Basic Chainlit app

You can see an example of a basic chat app by running the following: 
uv run --with anthropic --with chainlit chainlit run chat_basic.py -w -h
Then navigate to http://localhost:8000

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Adding ClickHouse MCP server

Things get more interesting if we add the ClickHouse MCP server. You’ll need to update your .chainlit/config.toml file to allow the uv command to be used: 
[features.mcp.stdio]
    enabled = true
    # Only the executables in the allow list can be used for MCP stdio server.
    # Only need the base name of the executable, e.g. "npx", not "/usr/bin/npx".
    # Please don't comment this line for now, we need it to parse the executable name.
    allowed_executables = [ "npx", "uvx", "uv" ]
config.tomlFind the full config.toml file in the examples repository
There’s some glue code to get MCP servers working with Chainlit, so we’ll need to run this command to launch Chainlit instead: 
uv run --with anthropic --with chainlit chainlit run chat_mcp.py -w -h
To add the MCP server, click on the plug icon in the chat interface, and then add the following command to connect to use the ClickHouse SQL Playground: 
CLICKHOUSE_HOST=sql-clickhouse.clickhouse.com CLICKHOUSE_USER=demo CLICKHOUSE_PASSWORD= CLICKHOUSE_SECURE=true uv run --with mcp-clickhouse --python 3.13 mcp-clickhouse
If you want to use your own ClickHouse instance, you can adjust the values of the environment variables. You can then ask it questions like this:
  • Tell me about the tables that you have to query
  • What’s something interesting about New York taxis?