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Expose data, transformations, SQL queries, and job triggers—no glue code required. Deliver the right data to agents when and where they need it.\n\n## Overview\n\nKeboola MCP Server is an open-source bridge between your Keboola project and modern AI tools. It turns Keboola features—like storage access, SQL transformations, and job triggers—into callable tools for Claude, Cursor, CrewAI, LangChain, Amazon Q, and more.\n\n- [Quick Start](#-quick-start-remote-mcp-server-easiest-way)\n- [Local Setup](#local-mcp-server-setup-custom-or-dev-way)\n\n## Features\n\nWith the AI Agent and MCP Server, you can:\n\n- **Storage**: Query tables directly and manage table or bucket descriptions\n- **Components**: Create, List and inspect extractors, writers, data apps, and transformation configurations\n- **SQL**: Create SQL transformations with natural language\n- **Jobs**: Run components and transformations, and retrieve job execution details\n- **Flows**: Build and manage workflow pipelines using Conditional Flows and Orchestrator Flows.\n- **Data Apps**: Create, deploy and manage Keboola Streamlit Data Apps displaying your queries over storage data.\n- **Metadata**: Search, read, and update project documentation and object metadata using natural language\n- **Dev Branches**: Work safely in development branches outside of production, where all operations are scoped to the selected branch.\n\n---\n\n## 🚀 Quick Start: Remote MCP Server (Easiest Way)\n\nThe easiest way to use Keboola MCP Server is through our **Remote MCP Server**. This hosted solution eliminates the need for local setup, configuration, or installation.\n\n### What is the Remote MCP Server?\n\nOur remote server is hosted on every multi-tenant Keboola stack and supports OAuth authentication. You can connect to it from any AI assistant that supports remote Streamable HTTP connection and OAuth authentication.\n\n### How to Connect\n\n1. **Get your remote server URL**: Navigate to your Keboola Project Settings → `MCP Server` tab\n2. **Copy the server URL**: It will look like `https://mcp..keboola.com/mcp`\n3. **Configure your AI assistant**: Paste the URL into your AI assistant's MCP settings\n4. **Authenticate**: You'll be prompted to authenticate with your Keboola account and select your project\n\n### Supported Clients\n\n- **[Cursor](https://cursor.com)**: Use the \"Install In Cursor\" button in your project's MCP Server settings or click\n this button\n [![Install MCP Server](https://cursor.com/deeplink/mcp-install-dark.svg)](cursor://anysphere.cursor-deeplink/mcp/install?name=keboola&config=eyJ1cmwiOiJodHRwczovL21jcC51cy1lYXN0NC5nY3Aua2Vib29sYS5jb20vbWNwIn0%3D)\n- **[Claude Desktop](https://claude.ai)**: Add the integration via Settings → Integrations\n- **[Claude Code](https://www.anthropic.com/)**: Install using `claude mcp add --transport http keboola ` (see below for details)\n- **[Windsurf](https://windsurf.ai)**: Configure with the remote server URL\n- **[Make](https://make.com)**: Configure with the remote server URL\n- **Other MCP clients**: Configure with the remote server URL\n\n#### Claude Code Setup\n\nClaude Code is a command-line interface tool that allows you to interact with Claude using your terminal. You can install the Keboola MCP Server integration using a simple command.\n\n**Installation:**\n\nRun the following command in your terminal, replacing `` with your Keboola region:\n\n```bash\nclaude mcp add --transport http keboola https://mcp..keboola.com/mcp\n```\n\n**Region-specific commands:**\n\n| Region | Installation Command |\n|--------|----------------------|\n| US Virginia AWS | `claude mcp add --transport http keboola https://mcp.keboola.com/mcp` |\n| US Virginia GCP | `claude mcp add --transport http keboola https://mcp.us-east4.gcp.keboola.com/mcp` |\n| EU Frankfurt AWS | `claude mcp add --transport http keboola https://mcp.eu-central-1.keboola.com/mcp` |\n| EU Ireland Azure | `claude mcp add --transport http keboola https://mcp.north-europe.azure.keboola.com/mcp` |\n| EU Frankfurt GCP | `claude mcp add --transport http keboola https://mcp.europe-west3.gcp.keboola.com/mcp` |\n\n**Usage:**\n\nOnce installed, you can use the Keboola MCP Server in Claude Code by typing `/mcp` in your conversation and selecting the Keboola tools you want to use.\n\n**Authentication:**\n\nWhen you first use the Keboola MCP Server in Claude Code, a browser window will open prompting you to:\n1. Log in with your Keboola account\n2. Select the project you want to connect to\n3. Authorize the connection\n\nAfter authentication, you can start using Keboola tools directly from Claude Code.\n\nFor detailed setup instructions and region-specific URLs, see our [Remote Server Setup documentation](https://help.keboola.com/ai/mcp-server/#remote-server-setup).\n\n### Using Development Branches\nYou can work safely in [Keboola development branches](https://help.keboola.com/components/branches/) without affecting your production data. The remotely hosted MCP Servers respect the `KBC_BRANCH_ID` parameter and will scope all operations to the specified branch. You can find the development branch ID in the URL when navigating to the development branch in the UI, for example: `https://connection.us-east4.gcp.keboola.com/admin/projects/PROJECT_ID/branch/BRANCH_ID/dashboard`. The branch ID must be included in each request using the header `X-Branch-Id: `, otherwise the MCP Server uses production branch as default. This should be managed by the AI client or the environment handling the server connection.\n\n### Tool Authorization and Access Control\n\nWhen using HTTP-based transports (Streamable HTTP), you can control which tools are available to clients using HTTP headers. This is useful for restricting AI agent capabilities or enforcing compliance policies.\n\n#### Authorization Headers\n\n| Header | Description | Example |\n|--------|-------------|---------|\n| `X-Allowed-Tools` | Comma-separated list of allowed tools | `get_configs,get_buckets,query_data` |\n| `X-Disallowed-Tools` | Comma-separated list of tools to exclude | `create_config,run_job` |\n| `X-Read-Only-Mode` | Restrict to read-only tools only | `true`, `1`, or `yes` |\n\n#### Filter Behavior\n\nFilters apply in order: allowed → read-only intersection → disallowed exclusion. Empty headers = no restriction.\n\n#### Read-Only Tools\n\nRead-only tools are those annotated with `readOnlyHint=True`. These tools only retrieve information without making any changes to your Keboola project. For the current list of read-only tools, see the [TOOLS.md](https://github.com/keboola/mcp-server/blob/main/TOOLS.md) file which is an auto-generated snapshot of the actual tool set.\n\n#### Example: Read-Only Access\n\n```\nX-Read-Only-Mode: true\n```\n\nFor detailed documentation, see [developers.keboola.com/integrate/mcp/#tool-authorization-and-access-control](https://developers.keboola.com/integrate/mcp/#tool-authorization-and-access-control).\n\n---\n\n## Local MCP Server Setup (Custom or Dev Way)\n\nRun the MCP server on your own machine for full control and easy development. Choose this when you want to customize tools, debug locally, or iterate quickly. You’ll clone the repo, set Keboola credentials via environment variables or headers depending on the server transport, install dependencies, and start the server. This approach offers maximum flexibility (custom tools, local logging, offline iteration) but requires manual setup and you manage updates and secrets yourself.\n\nThe server supports multiple **transport** options, which can be selected by providing the `--transport ` argument when starting the server:\n- `stdio` - Default when `--transport` is not specified. Standard input/output, typically used for local deployment with a single client.\n- `streamable-http` - Runs the server remotely over HTTP with a bidirectional streaming channel, allowing the client and server to continuously exchange messages. Connect via /mcp (e.g., http://localhost:8000/mcp).\n- `http-compat` - An alias for `streamable-http`, kept for backwards compatibility.\n\nFor client–server communication, Keboola credentials must be provided to enable working with your project in your Keboola Region. The following are required: `KBC_STORAGE_TOKEN`, `KBC_STORAGE_API_URL`, `KBC_WORKSPACE_SCHEMA` and optionally `KBC_BRANCH_ID`. You can provide these in two ways:\n- For personal use (mainly with stdio transport): set the environment variables before starting the server. All requests will reuse these predefined credentials.\n- For multi-user use: include the variables in the request headers so that each request uses the credentials provided with it.\n\n\n### KBC_STORAGE_TOKEN\n\nThis is your authentication token for Keboola:\n\nFor instructions on how to create and manage Storage API tokens, refer to the [official Keboola documentation](https://help.keboola.com/management/project/tokens/).\n\n**Note**: If you want the MCP server to have limited access, use custom storage token, if you want the MCP to access everything in your project, use the master token.\n\n### KBC_WORKSPACE_SCHEMA\n\nThis identifies your workspace in Keboola and is used for SQL queries. However, this is **only required if you're using a custom storage token** instead of the Master Token:\n\n- If using [Master Token](https://help.keboola.com/management/project/tokens/#master-tokens): The workspace is created automatically behind the scenes\n- If using [custom storage token](https://help.keboola.com/management/project/tokens/#limited-tokens): Follow this [Keboola guide](https://help.keboola.com/tutorial/manipulate/workspace/) to get your KBC_WORKSPACE_SCHEMA\n\n**Note**: When creating a workspace manually, check Grant read-only access to all Project data option\n\n**Note**: KBC_WORKSPACE_SCHEMA is called Dataset Name in BigQuery workspaces, you simply click connect and copy the Dataset Name\n\n### KBC_STORAGE_API_URL (Keboola Region)\n\nYour Keboola Region API URL depends on your deployment region. You can determine your region by looking at the URL in your browser when logged into your Keboola project:\n\n| Region | API URL |\n|--------|---------|\n| AWS North America | `https://connection.keboola.com` |\n| AWS Europe | `https://connection.eu-central-1.keboola.com` |\n| Google Cloud EU | `https://connection.europe-west3.gcp.keboola.com` |\n| Google Cloud US | `https://connection.us-east4.gcp.keboola.com` |\n| Azure EU | `https://connection.north-europe.azure.keboola.com` |\n\n### KBC_BRANCH_ID (Optional)\n\nTo operate on a specific [Keboola development branch](https://help.keboola.com/components/branches/), set the branch ID using the `KBC_BRANCH_ID` parameter. The MCP server scopes its functionality to the specified branch, ensuring all changes remain isolated and do not impact the production branch.\n\n- If not provided, the server uses the production branch by default.\n- For development work, set `KBC_BRANCH_ID` to the numeric ID of your branch (e.g., `123456`). You can find the development branch ID in the URL when navigating to the development branch in the UI, for example: `https://connection.us-east4.gcp.keboola.com/admin/projects/PROJECT_ID/branch/BRANCH_ID/dashboard`.\n- On remote transports, you can override per-request with the HTTP header `X-Branch-Id: ` or `KBC_BRANCH_ID: `.\n\n\n### Installation\n\nMake sure you have:\n\n- [ ] Python 3.10+ installed\n- [ ] Access to a Keboola project with admin rights\n- [ ] Your preferred MCP client (Claude, Cursor, etc.)\n\n**Note**: Make sure you have `uv` installed. The MCP client will use it to automatically download and run the Keboola MCP Server.\n**Installing uv**:\n\n*macOS/Linux*:\n\n```bash\n#if homebrew is not installed on your machine use:\n# /bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\"\n\n# Install using Homebrew\nbrew install uv\n```\n\n*Windows*:\n\n```powershell\n# Using the installer script\npowershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"\n\n# Or using pip\npip install uv\n\n# Or using winget\nwinget install --id=astral-sh.uv -e\n```\n\nFor more installation options, see the [official uv documentation](https://docs.astral.sh/uv/getting-started/installation/).\n\n\n### Running Keboola MCP Server\n\nThere are four ways to use the Keboola MCP Server, depending on your needs:\n\n### Option A: Integrated Mode (Recommended)\n\nIn this mode, Claude or Cursor automatically starts the MCP server for you. **You do not need to run any commands in your terminal**.\n\n1. Configure your MCP client (Claude/Cursor) with the appropriate settings\n2. The client will automatically launch the MCP server when needed\n\n#### Claude Desktop Configuration\n\n1. Go to Claude (top left corner of your screen) -> Settings → Developer → Edit Config (if you don't see the claude_desktop_config.json, create it)\n2. Add the following configuration:\n3. Restart Claude desktop for changes to take effect\n\n```json\n{\n \"mcpServers\": {\n \"keboola\": {\n \"command\": \"uvx\",\n \"args\": [\"keboola_mcp_server --transport \"],\n \"env\": {\n \"KBC_STORAGE_API_URL\": \"https://connection.YOUR_REGION.keboola.com\",\n \"KBC_STORAGE_TOKEN\": \"your_keboola_storage_token\",\n \"KBC_WORKSPACE_SCHEMA\": \"your_workspace_schema\",\n \"KBC_BRANCH_ID\": \"your_branch_id_optional\"\n }\n }\n }\n}\n```\n\nConfig file locations:\n\n- **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`\n- **Windows**: `%APPDATA%\\Claude\\claude_desktop_config.json`\n\n#### Cursor Configuration\n\n1. Go to Settings → MCP\n2. Click \"+ Add new global MCP Server\"\n3. Configure with these settings:\n\n```json\n{\n \"mcpServers\": {\n \"keboola\": {\n \"command\": \"uvx\",\n \"args\": [\"keboola_mcp_server --transport \"],\n \"env\": {\n \"KBC_STORAGE_API_URL\": \"https://connection.YOUR_REGION.keboola.com\",\n \"KBC_STORAGE_TOKEN\": \"your_keboola_storage_token\",\n \"KBC_WORKSPACE_SCHEMA\": \"your_workspace_schema\",\n \"KBC_BRANCH_ID\": \"your_branch_id_optional\"\n }\n }\n }\n}\n```\n\n**Note**: Use short, descriptive names for MCP servers. Since the full tool name includes the server name and must stay under ~60 characters, longer names may be filtered out in Cursor and will not be displayed to the Agent.\n\n\n#### Cursor Configuration for Windows WSL\n\nWhen running the MCP server from Windows Subsystem for Linux with Cursor AI, use this configuration:\n\n```json\n{\n \"mcpServers\": {\n \"keboola\":{\n \"command\": \"wsl.exe\",\n \"args\": [\n \"bash\",\n \"-c '\",\n \"export KBC_STORAGE_API_URL=https://connection.YOUR_REGION.keboola.com &&\",\n \"export KBC_STORAGE_TOKEN=your_keboola_storage_token &&\",\n \"export KBC_WORKSPACE_SCHEMA=your_workspace_schema &&\",\n \"export KBC_BRANCH_ID=your_branch_id_optional &&\",\n \"/snap/bin/uvx keboola_mcp_server --transport \",\n \"'\"\n ]\n }\n }\n}\n```\n\n### Option B: Local Development Mode\n\nFor developers working on the MCP server code itself:\n\n1. Clone the repository and set up a local environment\n2. Configure Claude/Cursor to use your local Python path:\n\n```json\n{\n \"mcpServers\": {\n \"keboola\": {\n \"command\": \"/absolute/path/to/.venv/bin/python\",\n \"args\": [\n \"-m\",\n \"keboola_mcp_server --transport \"\n ],\n \"env\": {\n \"KBC_STORAGE_API_URL\": \"https://connection.YOUR_REGION.keboola.com\",\n \"KBC_STORAGE_TOKEN\": \"your_keboola_storage_token\",\n \"KBC_WORKSPACE_SCHEMA\": \"your_workspace_schema\",\n \"KBC_BRANCH_ID\": \"your_branch_id_optional\"\n }\n }\n }\n}\n```\n\n### Option C: Manual CLI Mode (For Testing Only)\n\nYou can run the server manually in a terminal for testing or debugging:\n\n```bash\n# Set environment variables\nexport KBC_STORAGE_API_URL=https://connection.YOUR_REGION.keboola.com\nexport KBC_STORAGE_TOKEN=your_keboola_storage_token\nexport KBC_WORKSPACE_SCHEMA=your_workspace_schema\nexport KBC_BRANCH_ID=your_branch_id_optional\n\nuvx keboola_mcp_server --transport streamable-http\n```\n\n> **Note**: This mode is primarily for debugging or testing. For normal use with Claude or Cursor,\n> you do not need to manually run the server.\n\n> **Note**: The server will use the Streamable HTTP transport and listen on `localhost:8000` for incoming connections at `/mcp`.\n> You can use `--port` and `--host` parameters to make it listen elsewhere.\n\n### Option D: Using Docker\n\n```shell\ndocker pull keboola/mcp-server:latest\n\ndocker run \\\n --name keboola_mcp_server \\\n --rm \\\n -it \\\n -p 127.0.0.1:8000:8000 \\\n -e KBC_STORAGE_API_URL=\"https://connection.YOUR_REGION.keboola.com\" \\\n -e KBC_STORAGE_TOKEN=\"YOUR_KEBOOLA_STORAGE_TOKEN\" \\\n -e KBC_WORKSPACE_SCHEMA=\"YOUR_WORKSPACE_SCHEMA\" \\\n -e KBC_BRANCH_ID=\"YOUR_BRANCH_ID_OPTIONAL\" \\\n keboola/mcp-server:latest \\\n --transport streamable-http \\\n --host 0.0.0.0\n```\n\n> **Note**: The server will use the Streamable HTTP transport and listen on `localhost:8000` for incoming connections at `/mcp`.\n> You can change `-p` to map the container's port somewhere else.\n\n### Do I Need to Start the Server Myself?\n\n| Scenario | Need to Run Manually? | Use This Setup |\n|----------|----------------------|----------------|\n| Using Claude/Cursor | No | Configure MCP in app settings |\n| Developing MCP locally | No (Claude starts it) | Point config to python path |\n| Testing CLI manually | Yes | Use terminal to run |\n| Using Docker | Yes | Run docker container |\n\n## Using MCP Server\n\nOnce your MCP client (Claude/Cursor) is configured and running, you can start querying your Keboola data:\n\n### Verify Your Setup\n\nYou can start with a simple query to confirm everything is working:\n\n```text\nWhat buckets and tables are in my Keboola project?\n```\n\n### Examples of What You Can Do\n\n**Data Exploration:**\n\n- \"What tables contain customer information?\"\n- \"Run a query to find the top 10 customers by revenue\"\n\n**Data Analysis:**\n\n- \"Analyze my sales data by region for the last quarter\"\n- \"Find correlations between customer age and purchase frequency\"\n\n**Data Pipelines:**\n\n- \"Create a SQL transformation that joins customer and order tables\"\n- \"Start the data extraction job for my Salesforce component\"\n\n## Compatibility\n\n### MCP Client Support\n\n| **MCP Client** | **Support Status** | **Connection Method** |\n|----------------|-------------------|----------------------|\n| Claude (Desktop & Web) | ✅ supported | stdio |\n| Cursor | ✅ supported | stdio |\n| Windsurf, Zed, Replit | ✅ Supported | stdio |\n| Codeium, Sourcegraph | ✅ Supported | Streamable HTTP |\n| Custom MCP Clients | ✅ Supported | Streamable HTTP or stdio |\n\n## Supported Tools\n\n**Note:** Your AI agents will automatically adjust to new tools.\n\nFor a complete list of available tools with detailed descriptions, parameters, and usage examples, see [TOOLS.md](https://github.com/keboola/mcp-server/blob/main/TOOLS.md).\n\n## Troubleshooting\n\n### Common Issues\n\n| Issue | Solution |\n|-------|----------|\n| **Authentication Errors** | Verify `KBC_STORAGE_TOKEN` is valid |\n| **Workspace Issues** | Confirm `KBC_WORKSPACE_SCHEMA` is correct |\n| **Connection Timeout** | Check network connectivity |\n\n## Development\n\n### Installation\n\nBasic setup:\n\n```bash\nuv sync --extra dev\n```\n\nWith the basic setup, you can use `uv run tox` to run tests and check code style.\n\nRecommended setup:\n\n```bash\nuv sync --extra dev --extra tests --extra integtests --extra codestyle\n```\n\nWith the recommended setup, packages for testing and code style checking will be installed which allows IDEs like\nVsCode or Cursor to check the code or run tests during development.\n\n### Integration tests\n\nTo run integration tests locally, use `uv run tox -e integtests`.\nNOTE: You will need to set the following environment variables:\n\n- `INTEGTEST_POOL_STORAGE_API_URL`\n- `INTEGTEST_STORAGE_TOKENS`\n- `INTEGTEST_STORAGE_TOKEN_STORAGE_BRANCHES`\n\nIn order to get these values, you need dedicated Keboola projects for integration tests.\nEach test session creates its own read-only workspace, so no workspace schema needs to be\nconfigured. See `integtests/README.md` for detailed setup instructions and design documentation.\n\n### Updating `uv.lock`\n\nUpdate the `uv.lock` file if you have added or removed dependencies. Also consider updating the lock with newer dependency\nversions when creating a release (`uv lock --upgrade`).\n\n### Updating Tool Documentation\n\nWhen you make changes to any tool descriptions (docstrings in tool functions), you must regenerate the `TOOLS.md` documentation file to reflect these changes:\n\n```bash\nuv run python -m src.keboola_mcp_server.generate_tool_docs\n```\n\n### Releasing\n\nWe do **not** cut a release for every merged PR. Work lands on the trunk (`main`)\ncontinuously, and we release periodically once changes have been re-tested together —\nthis avoids breaking working setups for users.\n\nA release is made by pushing **one or two git tags**:\n\n- `vX.Y.Z` — the MCP server release (always)\n- `agent-vX.Y.Z` — the In Platform Agent release (only when the agent is being released too)\n\nEither tag triggers `release.yml` CI, which builds and publishes the Docker image. KaiBench\nruns only on production `vX.Y.Z` tags (not `agent-vX.Y.Z`, and not `-dev.` prereleases). Use\nthe `release-notes` skill — it prepares the release notes and draft PR and walks through\ntagging both `vX.Y.Z` and `agent-vX.Y.Z`.\n\n## Support and Feedback\n\n**⭐ The primary way to get help, report bugs, or request features is by [opening an issue on GitHub](https://github.com/keboola/mcp-server/issues/new). ⭐**\n\nThe development team actively monitors issues and will respond as quickly as possible. For general information about Keboola, please use the resources below.\n\n## Resources\n\n- [User Documentation](https://help.keboola.com/)\n- [Developer Documentation](https://developers.keboola.com/)\n- [Keboola Platform](https://www.keboola.com)\n- [Issue Tracker](https://github.com/keboola/mcp-server/issues/new) ← **Primary contact method for MCP Server**\n\n## Connect\n\n- [LinkedIn](https://www.linkedin.com/company/keboola)\n- [Twitter](https://x.com/keboola)\n- [Changelog](https://changelog.keboola.com/)\n\n\n[//]: # (mcp-name: com.keboola/mcp -- keep this line for registry.modelcontextprotocol.io)\n","changelogMarkdown","videoKey","requiredSecretsJson","publishedAt","priceCents","trialDays",0,"categoryDisplayName","Data & Analytics","upstream",{"_95":96,"_97":98,"_99":100,"_101":102,"_103":-5},"requiredSecrets",[],"tags",[],"assets",[],"stats",{"_93":80,"_94":80},"ratingAvg","reviewCount","topics",[104,105,106,107,21,108],"license","MIT","language","Python","registryName","com.keboola/mcp","homepage","data-platform","etl-pipeline","keboola-platform","mcp","model-context-protocol","ENV",{"_111":112,"_113":114,"_115":116},"ENVIRONMENT","production","TURNSTILE_SITE_KEY","0x4AAAAAAB8-rEI2gNDf_QhU","SENTRY_DSN","https://45fd1229f78089d5c3ee78be128030e1@o4510583261757440.ingest.de.sentry.io/4510584253448272"]