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Provides access to Keboola data sources, transformations, jobs, and workflows via AI agents and MCP clients.
Configuration
View docs{
"mcpServers": {
"keboola-mcp-server": {
"url": "https://mcp.<YOUR_REGION>.keboola.com/mcp",
"headers": {
"KBC_BRANCH_ID": "YOUR_BRANCH_ID_OPTIONAL",
"KBC_STORAGE_TOKEN": "YOUR_KBC_STORAGE_TOKEN",
"KBC_STORAGE_API_URL": "https://connection.YOUR_REGION.keboola.com",
"KBC_WORKSPACE_SCHEMA": "YOUR_WORKSPACE_SCHEMA"
}
}
}
}Keboola MCP Server is an open-source bridge that lets you connect Keboola data, SQL transformations, and workflow components to modern AI assistants and MCP clients. You can expose data, run transformations and jobs, and orchestrate data flows from your AI agents with no glue code needed.
Connect Keboola to your AI assistants and MCP clients to access data, run SQL transformations, trigger jobs, and manage workflows. Start by connecting to the remote MCP server or running a local instance, then configure your client to use the provided MCP endpoint. Once connected, you can ask your AI agent to query tables, run a SQL transformation, trigger a data extraction job, or manage workflows just like you would with any API-equipped tool.
Prerequisites: you need a Keboola project with admin rights, Python 3.10+ (or a suitable runtime for your chosen transport), and an MCP client (Claude, Cursor, Windsurf, VS Code, or another client). You may also use uvx to run the MCP server locally.
Choose your setup path and follow the steps below.
# Remote (recommended) setup via the remote MCP server
# No local installation required; configure your MCP client to connect to the remote endpoint
# Example remote endpoint for your region (you will replace YOUR_REGION):
# https://mcp.YOUR_REGION.keboola.com/mcp
# Local development or testing requires running the MCP server yourself (see options B-D below)There are multiple ways to use Keboola MCP Server depending on your needs. You can opt for the remote hosted server for zero-setup usage, or run a local MCP server for full control and development. Each method provides a compatible MCP endpoint for your clients.
Query storage tables directly and manage table or bucket descriptions.
Create, list and inspect extractors, writers, data apps, and transformation configurations.
Create SQL transformations with natural language requests to transform data.
Run components and transformations, and retrieve job execution details.
Build Conditional Flows and Orchestrator Flows to manage workflows.
Create, deploy and manage Keboola Streamlit Data Apps displaying your data queries.
Search, read, and update project documentation and object metadata using natural language.
Work safely in development branches with scoped operations using branch IDs.