The Keboola MCP Server acts as a crucial bridge, connecting AI models to the wealth of data residing within Keboola Connection. As an MCP server, it empowers models to seamlessly access and utilize data from the Keboola Storage API. This includes listing buckets and tables, retrieving metadata, previewing data, and exporting tables to CSV format.

By integrating with Keboola Connection, this server eliminates data silos and unlocks the potential for AI-driven insights. Developers can leverage this tool to build AI applications that directly benefit from Keboola's robust data management capabilities. The server requires a Keboola Storage API token and a Snowflake Read Only Workspace for secure access. It can be easily installed via Smithery or manually, and integrates smoothly with clients like Claude Desktop and Cursor AI using standard I/O or Server-Sent Events (SSE). This enables a streamlined workflow for AI model development and deployment.

Data Discovery and Metadata Retrieval

The keboola-mcp-server allows AI models to dynamically discover and understand the structure of data stored within Keboola Connection. It achieves this by providing endpoints to list buckets and tables, and to retrieve detailed metadata about each, including schema information. This eliminates the need for hardcoding data locations or schemas into the AI model, making it more adaptable to changes in the data environment. For example, an AI model designed to analyze customer churn can automatically adapt to a new data table containing updated customer information, without requiring code modifications. The server leverages the Keboola Storage API to fetch this metadata, ensuring that the AI model always has an up-to-date view of the available data. This dynamic discovery is crucial for building robust and maintainable AI applications.

Secure Data Access and Preview

This feature enables AI models to securely access and preview data stored in Keboola Connection, adhering to defined access controls. The server uses a Keboola Storage API token for authentication, ensuring that the AI model only accesses data it is authorized to view. The preview functionality allows the AI model to sample data before processing it, which is useful for understanding data characteristics and optimizing processing strategies. For instance, an AI model tasked with sentiment analysis can preview a sample of customer reviews to understand the data format and distribution of sentiments before processing the entire dataset. This secure access and preview capability is essential for building trustworthy AI applications that comply with data governance policies. The server also supports the use of a read-only Snowflake workspace, further enhancing data security.

Data Export to Standard Formats

The keboola-mcp-server facilitates the export of data from Keboola Connection tables into CSV format, a widely supported format for data processing and analysis. This allows AI models to easily ingest data from Keboola, regardless of their specific data format requirements. The server handles the complexities of data extraction and formatting, providing a simple and consistent interface for AI models to access the data. For example, an AI model built using a framework that only supports CSV input can seamlessly access data stored in Keboola tables. This feature simplifies the integration of AI models with Keboola data and reduces the need for custom data transformation logic. The server uses the Keboola Storage API to efficiently export the data, minimizing the impact on Keboola Connection performance.

Integration with AI Client Frameworks

The keboola-mcp-server is designed for seamless integration with various AI client frameworks, such as Claude Desktop and Cursor AI. It supports multiple transport methods, including Standard I/O (stdio) and Server-Sent Events (SSE), allowing developers to choose the method that best suits their needs and the capabilities of their chosen AI client. The server provides detailed configuration instructions for each framework, simplifying the setup process and ensuring compatibility. For example, developers can easily configure the server to work with Claude Desktop by providing the path to the server executable and the necessary environment variables. This integration capability enables developers to leverage the power of Keboola data within their preferred AI development environment.

Technical Implementation

The keboola-mcp-server is implemented in Python and leverages the Keboola Storage API client library. It is designed to be lightweight and easy to deploy, with minimal dependencies. The server uses a modular architecture, allowing developers to easily extend its functionality with custom endpoints and data processing logic. It also includes comprehensive testing and formatting tools to ensure code quality and maintainability. The server can be installed manually or via Smithery, a tool for automating the installation of MCP servers. This technical implementation ensures that the server is reliable, scalable, and easy to maintain.