Bridging AI and Analytics: A Deep Dive into ClickHouse MCP Server

Community Article Published May 1, 2025

In the rapidly evolving landscape of data analytics and artificial intelligence, the ability to seamlessly integrate powerful tools is paramount. ClickHouse, renowned for its exceptional speed and efficiency in handling large-scale analytical queries, stands as a cornerstone for many data-driven organizations. Simultaneously, AI assistants like Claude are transforming how users interact with information and execute tasks. But how can we bridge the gap, enabling sophisticated AI assistants to securely and effectively interact with the data powerhouse that is Clickhouse? The answer lies in the Multi-Client Protocol (MCP) and, specifically, the mcp-clickhouse server project.

This article provides a comprehensive guide to understanding MCP, installing the mcp-clickhouse server, and configuring it to empower AI assistants like Claude Desktop to interact directly with your ClickHouse databases.

Tired of Postman? Want a decent postman alternative that doesn't suck?

Apidog is a powerful all-in-one API development platform that's revolutionizing how developers design, test, and document their APIs.

Unlike traditional tools like Postman, Apidog seamlessly integrates API design, automated testing, mock servers, and documentation into a single cohesive workflow. With its intuitive interface, collaborative features, and comprehensive toolset, Apidog eliminates the need to juggle multiple applications during your API development process.

Whether you're a solo developer or part of a large team, Apidog streamlines your workflow, increases productivity, and ensures consistent API quality across your projects.

What is the Multi-Client Protocol (MCP)?

Before diving into the specifics of the ClickHouse integration, it's essential to grasp the concept of the Multi-Client Protocol (MCP). At its core, MCP is a standardized communication framework designed to allow AI assistants or other client applications to securely interact with external tools, services, and APIs.

Think of it as a universal translator and secure gateway. Instead of embedding complex logic, credentials, and specific client libraries for every potential tool directly into the AI assistant, MCP introduces an intermediary layer: the MCP server.

The MCP Architecture:

Client (e.g., Claude Desktop): The AI assistant understands the user's request (e.g., "List tables in my ClickHouse database"). It knows about available MCP servers and the tools they offer.
MCP Server (e.g., mcp-clickhouse): A dedicated process that listens for requests from the client via the MCP protocol. It exposes a specific set of "tools" (functions) that it can perform. It holds the necessary configuration and credentials (ideally securely managed) to interact with the target service (e.g., ClickHouse).
Target Service (e.g., ClickHouse): The actual service or database that the MCP server interacts with on behalf of the client.

Why Use MCP?

MCP offers several significant advantages:

Security: Credentials and sensitive configuration details (like database hostnames, usernames, passwords) are managed within the MCP server's environment, not directly exposed to or stored within the main AI client application. This significantly enhances security posture. MCP servers can also enforce specific restrictions, like the mcp-clickhouse server running queries in read-only mode.
Standardization: MCP provides a consistent protocol for interaction. AI clients can communicate with any MCP-compliant server using the same underlying mechanism, simplifying the integration of new tools.
Modularity and Isolation: Each MCP server runs as a separate process. This isolates dependencies and potential issues. If one MCP server fails, it doesn't necessarily bring down the entire AI assistant or other tools. Updates to a tool or its client library only require updating the specific MCP server.
Simplified Client Logic: The AI client doesn't need to know the intricacies of connecting to ClickHouse, handling different authentication methods, or managing specific database driver versions. It only needs to know how to speak MCP and which tools the mcp-clickhouse server provides.

In essence, MCP acts as a secure and standardized bridge, allowing powerful AI clients to leverage the capabilities of external systems like ClickHouse without compromising security or creating monolithic, hard-to-maintain applications.

Introducing `mcp-clickhouse`: Your Gateway to ClickHouse

The mcp-clickhouse project, available on GitHub, is a concrete implementation of an MCP server specifically designed for ClickHouse. Built using Python, it acts as the intermediary enabling AI assistants that support MCP (like Claude Desktop) to execute commands and queries against a ClickHouse cluster.

Key Features and Tools:

The mcp-clickhouse server exposes a set of well-defined tools that the AI client can invoke:

run_select_query:
- Purpose: Executes SQL SELECT queries against the configured ClickHouse cluster.
- Input: Requires a sql parameter (string) containing the query to execute.
- Security: Crucially, all queries executed through this tool are run with the ClickHouse setting readonly = 1. This is a vital security measure, ensuring that the AI assistant, through the MCP server, cannot accidentally or maliciously modify data (INSERT, UPDATE, DELETE, ALTER, etc.). It restricts operations to data retrieval only.
list_databases:
- Purpose: Retrieves a list of all available databases within the ClickHouse cluster that the configured user has access to.
- Input: No input parameters are required.
list_tables:
- Purpose: Lists all tables within a specified database.
- Input: Requires a database parameter (string) indicating the name of the database whose tables should be listed.

These tools provide fundamental capabilities for exploring database structure and retrieving data, all within the secure, read-only confines enforced by the MCP server.

Installation and Configuration: Setting Up `mcp-clickhouse`

The primary method for using mcp-clickhouse is by integrating it with an MCP-compatible client application, such as Claude Desktop. The configuration involves telling the client application how to find and run the mcp-clickhouse server process and providing the necessary environment variables for it to connect to your ClickHouse instance.

Prerequisites:

Python: The server is written in Python. While the README mentions Python 3.13 in its examples, ensure you have a compatible Python version installed.
uv: The configuration examples use uv, a relatively new and fast Python package installer and virtual environment manager. You'll need uv installed on your system. Follow the official installation instructions for uv if you don't have it. On macOS or Linux, you can typically install it via pip or other package managers.
Claude Desktop (or other MCP Client): You need the client application that will communicate with the MCP server. This guide focuses on the Claude Desktop configuration provided in the mcp-clickhouse README.

Configuration Steps:

Locate the Client Configuration File: Find the configuration file for your MCP client application. For Claude Desktop, the locations are:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%/Claude/claude_desktop_config.json Open this JSON file in a text editor.

Add the mcpServers Configuration: Within the main JSON object of the configuration file, you need to add or modify the mcpServers key. Add an entry for mcp-clickhouse like the example below:

{
  "mcpServers": {
    "mcp-clickhouse": {
      "command": "uv", // Note: May need to be an absolute path
      "args": [
        "run",
        "--with",
        "mcp-clickhouse",
        "--python",
        "3.13", // Specify the Python version if needed
        "mcp-clickhouse"
      ],
      "env": {
        "CLICKHOUSE_HOST": "<your-clickhouse-host>",
        "CLICKHOUSE_PORT": "<optional-port>", // Optional, defaults apply
        "CLICKHOUSE_USER": "<your-clickhouse-user>",
        "CLICKHOUSE_PASSWORD": "<your-clickhouse-password>",
        "CLICKHOUSE_SECURE": "true", // Default: true (use "false" for http)
        "CLICKHOUSE_VERIFY": "true", // Default: true (use "false" to skip SSL verify)
        "CLICKHOUSE_CONNECT_TIMEOUT": "30", // Default: 30 seconds
        "CLICKHOUSE_SEND_RECEIVE_TIMEOUT": "300", // Default: 300 seconds
        "CLICKHOUSE_DATABASE": "<optional-default-database>" // Optional
      }
    }
    // ... other MCP servers if any ...
  }
  // ... other Claude Desktop settings ...
}

Configure Environment Variables (env block): This is the most critical part, where you tell mcp-clickhouse how to connect to your specific ClickHouse instance.
- CLICKHOUSE_HOST (Required): The hostname or IP address of your ClickHouse server (e.g., my-clickhouse.example.com, localhost, your-instance.clickhouse.cloud).
- CLICKHOUSE_USER (Required): The username for authenticating with ClickHouse.
- CLICKHOUSE_PASSWORD (Required): The password corresponding to the CLICKHOUSE_USER.
  
  Security Warning: As strongly cautioned in the project README, treat the database user configured here like any external client. Grant it only the minimum necessary privileges required for its operation (primarily SELECT permissions on the desired tables/databases, and permissions to view database/table lists). Never use administrative or default users (default user is okay only for temporary local development testing). Create a dedicated, least-privilege user for the MCP server connection.
- CLICKHOUSE_PORT (Optional): The port number for the ClickHouse HTTP(S) interface.
  - Defaults to 8443 if CLICKHOUSE_SECURE is true (HTTPS).
  - Defaults to 8123 if CLICKHOUSE_SECURE is false (HTTP).
  - You only need to set this if your ClickHouse instance uses a non-standard port for its HTTP interface.
- CLICKHOUSE_SECURE (Optional): Controls whether to use HTTPS.
  - Default: "true". Set to "false" if your ClickHouse instance uses plain HTTP (e.g., a local development setup without SSL).
- CLICKHOUSE_VERIFY (Optional): Controls SSL certificate verification when using HTTPS.
  - Default: "true". Set to "false" only if absolutely necessary (e.g., local development with self-signed certificates). Disabling verification in production environments is strongly discouraged as it exposes you to man-in-the-middle attacks.
- CLICKHOUSE_CONNECT_TIMEOUT (Optional): Maximum time in seconds to wait for establishing a connection.
  - Default: "30". Increase if you experience initial connection timeouts on slow networks or busy servers.
- CLICKHOUSE_SEND_RECEIVE_TIMEOUT (Optional): Maximum time in seconds to wait for data to be sent or received during a query.
  - Default: "300" (5 minutes). Increase if you need to run very long-running SELECT queries that might exceed this limit.
- CLICKHOUSE_DATABASE (Optional): Specifies a default database to connect to. If set, queries that don't explicitly qualify table names will use this database. If not set, it relies on the ClickHouse server's default database for the user.
Example Configurations:
- For ClickHouse Cloud:
```
  "env": {
    "CLICKHOUSE_HOST": "your-instance.clickhouse.cloud",
    "CLICKHOUSE_USER": "mcp_readonly_user", // Your dedicated user
    "CLICKHOUSE_PASSWORD": "your-secure-password",
    // Secure defaults (HTTPS, verify) usually work fine
    "CLICKHOUSE_DATABASE": "your_analytics_db" // Optional
  }
```
- For Local Development (Docker, HTTP):
```
  "env": {
    "CLICKHOUSE_HOST": "localhost",
    "CLICKHOUSE_USER": "default", // Ok for local dev ONLY
    "CLICKHOUSE_PASSWORD": "clickhouse", // Example password
    "CLICKHOUSE_SECURE": "false", // Use HTTP
    "CLICKHOUSE_VERIFY": "false"  // No cert verification for local
  }
```
- For ClickHouse Playground (Testing):
```
  "env": {
    "CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
    "CLICKHOUSE_PORT": "8443", // Explicitly set, though default works
    "CLICKHOUSE_USER": "demo",
    "CLICKHOUSE_PASSWORD": "", // Playground demo user has no password
    "CLICKHOUSE_SECURE": "true",
    "CLICKHOUSE_VERIFY": "true"
  }
```
Specify Absolute Path for uv (Important!): The "command": "uv" entry might not work reliably if uv isn't in the default system PATH that Claude Desktop uses. The README explicitly recommends replacing "uv" with the absolute path to the uv executable.
- On macOS/Linux, open your terminal and run which uv. Copy the full path (e.g., /Users/youruser/.cargo/bin/uv or /opt/homebrew/bin/uv).
- Replace "uv" in the command field with this absolute path. Example:
```
"command": "/Users/youruser/.cargo/bin/uv",
```
- On Windows, find the path to uv.exe similarly and use the appropriate path format.
Restart the Client Application: Save the claude_desktop_config.json file. Close and restart Claude Desktop completely to ensure it reads the updated configuration and attempts to launch the mcp-clickhouse server process.

Using the `mcp-clickhouse` Server via Claude

Once the configuration is correctly set up and Claude Desktop is restarted, the mcp-clickhouse server should be running in the background, managed by Claude. You can now interact with it through natural language requests to Claude.

Claude is aware of the tools provided by the configured MCP servers. You can ask it to:

List databases: "List the databases available in ClickHouse."
List tables: "Show me the tables in the 'logs' database." or "What tables are in the 'default' database?"
Run queries: "Run the query SELECT event_date, count(*) FROM user_activity WHERE event_date >= today() - 7 GROUP BY event_date ORDER BY event_date" or "Count the number of rows in the 'metrics' table."

Claude will interpret your request, identify the appropriate tool (list_databases, list_tables, or run_select_query), construct the necessary MCP call (including parameters like the database name or the SQL query string), send it to the mcp-clickhouse server, receive the results, and present them back to you in a user-friendly format. Remember, all run_select_query operations are safely executed in read-only mode.

Development and Testing (For Contributors)

For those interested in contributing to the mcp-clickhouse project or running it locally for development outside of Claude Desktop, the README provides development setup instructions:

Start Test Cluster: Use Docker Compose within the test-services directory (docker compose up -d) to spin up a local ClickHouse instance for testing.
Environment File: Create a .env file in the repository root with local connection details (e.g., CLICKHOUSE_HOST=localhost, CLICKHOUSE_USER=default, etc.).
Install Dependencies: Use uv sync --all-extras --dev to install main and development dependencies into a virtual environment. Activate the environment (source .venv/bin/activate).
Run Server: Start the server locally using mcp dev mcp_clickhouse/mcp_server.py.
Run Tests: Execute the test suite using uv run pytest tests.
Linting: Check code style with uv run ruff check ..

Conclusion

The ClickHouse MCP server (mcp-clickhouse) represents a significant step forward in integrating powerful analytical databases with modern AI assistants. By leveraging the Multi-Client Protocol, it provides a secure, standardized, and efficient way for applications like Claude Desktop to explore ClickHouse schemas and execute read-only queries based on user requests. The configuration process, while detailed, primarily involves setting up the correct environment variables and ensuring the client application can locate and execute the uv command to run the server. By following the steps outlined in this guide and adhering to security best practices (especially regarding user privileges), users can unlock the ability to interact with their ClickHouse data directly from their AI assistant, streamlining workflows and making data insights more accessible than ever before.

Community

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

Your need to confirm your account before you can post a new comment.

· Sign up or log in to comment

Upvote