SingleStore MCP Server

Requirements

• Python >= v3.11.0
• uvx installed on your python environment
• Claude Desktop, Cursor, or another supported LLM client

Client Setup# 1. Init Command

The simplest way to set up the MCP server is to use the initialization command:

uvx singlestore-mcp-server init
```\n\n### 2. Installing via Smithery
To install `mcp-server-singlestore` automatically via [Smithery](https://smithery.ai/server/@singlestore-labs/mcp-server-singlestore):
```bash
npx -y @smithery/cli install @singlestore-labs/mcp-server-singlestore --client=
```\n\n### 3. Manual Configuration## Claude Desktop and Cursor
1. Add the following configuration to your client configuration file.
```json
{
  "mcpServers": {
    "singlestore-mcp-server": {
      "command": "uvx",
      "args": [
        "singlestore-mcp-server",
        "start"
      ]
    }
  }
}
```\n\n### 4. Using Docker## Building the Docker Image
To build the Docker image for the MCP server, run the following command in the project root:
```bash
docker build -t mcp-server-singlestore .
```\n\n#### Running the Docker Container
To run the Docker container, use the following command:
```bash
docker run -d \
- p 8000:8000 \
- e MCP_API_KEY="your_api_key_here" \
- it \
- -name mcp-server \
  mcp-server-singlestore
```\n\n## Components# Tools
The server implements the following tools:
- **workspace_groups_info**: Retrieve details about the workspace groups accessible to the user
- **workspaces_info**: Retrieve details about the workspaces in a specific workspace group
- **organization_info**: Retrieve details about the user's current organization
- **list_of_regions**: Retrieve a list of all regions that support workspaces for the user
- **execute_sql**: Execute SQL operations on a connected workspace
- **list_virtual_workspaces**: List all starter workspaces accessible to the user
- **create_virtual_workspace**: Create a new starter workspace with a user
- **execute_sql_on_virtual_workspace**: Execute SQL operations on a starter workspace
- **list_notebook_samples**: List all notebook samples available in SingleStore Spaces
- **create_notebook**: Create a new notebook in the user's personal space
- **list_personal_files**: List all files in the user's personal space
- **create_scheduled_job**: Create a new scheduled job to run a notebook
- **get_job_details**: Get details about a specific job
- **list_job_executions**: List execution history for a specific job\n\n## Development# Prerequisites
- Python >= 3.11
- [uv](https://docs.astral.sh/uv/) for dependency management\n\n### Setup
1. Clone the repository:
```bash
git clone https://github.com/singlestore-labs/mcp-server-singlestore.git
cd mcp-server-singlestore

1. Install dependencies:

uv sync --dev

1. Set up pre-commit hooks (optional but recommended):

uv run pre-commit install
```\n\n### Development Workflow
```bash\n\n# Quick quality checks (fast feedback)
./scripts/check.sh\n\n# Run tests independently
./scripts/test.sh\n\n# Comprehensive validation (before PRs)
./scripts/check-all.sh\n\n# Create and publish releases
./scripts/release.sh
```\n\n### Running Tests
```bash\n\n# Run test suite with coverage
./scripts/test.sh\n\n# Or use pytest directly
uv run pytest
```\n\n### Code Quality
We use [Ruff](https://docs.astral.sh/ruff/) for both linting and formatting:
```bash\n\n# Format code
uv run ruff format src/ tests/\n\n# Lint code
uv run ruff check src/ tests/\n\n# Lint and fix issues automatically
uv run ruff check --fix src/ tests/
```\n\n### Release Process
Releases are managed through git tags and automated PyPI publication:
1. **Create release**: `./scripts/release.sh` (interactive tool)
2. **Automatic publication**: Triggered by pushing version tags
3. **No manual PyPI uploads** - fully automated pipeline