Rockfish MCP Server

A Model Context Protocol server that provides tools to interact with the Rockfish AI platform for synthetic data generation, dataset management, and ML workflow orchestration.

Available Tools

Rockfish API — databases, worker sets, workflows, models, projects, datasets, organizations (22+ tools)

SDK (Synthetic Data Generation)

• obtain_train_config — generate training configuration with automatic column type detection
• update_train_config — modify training hyperparameters or field classifications
• start_training_workflow — start TabGAN training workflow
• get_workflow_logs — stream workflow logs with configurable level and timeout
• get_trained_model_id — extract trained model ID from completed workflow
• start_generation_workflow — start generation workflow from trained model
• obtain_synthetic_dataset_id — extract generated dataset ID from completed workflow
• plot_distribution — generate distribution plots comparing real and synthetic data
• get_marginal_distribution_score — calculate similarity score between real and synthetic data

Manta (Analytics & Scenarios) — requires MANTA_API_URL

• discover_schema — discover dataset schema
• generate_test_suite — generate test suites
• execute_query / execute_nl_query — run SQL or natural language queries
• inject_scenario — inject test scenarios into datasets

Installation

rockfish-mcp is not currently published to PyPI, so install it from source (below) and run it with uv run --directory /path/to/rockfish-mcp rockfish-mcp. The uvx rockfish-mcp and pip install rockfish-mcp forms only work once the package is published to an index.

From source (recommended)

git clone https://github.com/Rockfish-Data/rockfish-mcp.git
cd rockfish-mcp

1. Install dependencies (exact versions from uv.lock, including dev tools):

uv sync
source .venv/bin/activate  # optional; or prefix commands with `uv run`

For a runtime-only install (no dev tools), use uv sync --no-dev.

1. Set up environment variables:

cp .env.example .env
# Edit .env and add your Rockfish API key

Configuration

Create a .env file with your Rockfish API credentials:

ROCKFISH_API_KEY=your_api_key_here
ROCKFISH_API_URL=https://api.rockfish.ai

Optional settings:

ROCKFISH_ORGANIZATION_ID=your_organization_id_here
ROCKFISH_PROJECT_ID=your_project_id_here
MANTA_API_URL=https://manta.rockfish.ai

Usage with Claude Desktop

Add to your claude_desktop_config.json:

<details open> <summary>From a source checkout (uv run)</summary>

rockfish-mcp is not published to PyPI, so uvx rockfish-mcp fails with rockfish-mcp was not found in the package registry. Run it from a clone instead. Use uv run --directory rather than uvx --from, so the locked dependencies and the Rockfish SDK find-links index from pyproject.toml are honored. Use the absolute path to uv (which uv), since Claude Desktop does not inherit your shell PATH.

{
  "mcpServers": {
    "rockfish": {
      "command": "/opt/homebrew/bin/uv",
      "args": ["run", "--directory", "/path/to/rockfish-mcp", "rockfish-mcp"],
      "env": {
        "ROCKFISH_API_KEY": "your_api_key_here",
        "ROCKFISH_API_URL": "https://api.rockfish.ai"
      }
    }
  }
}

The first launch builds the environment and can take ~30 seconds; later launches are fast. </details>

<details> <summary>Using uvx (requires a published package)</summary>

{
  "mcpServers": {
    "rockfish": {
      "command": "uvx",
      "args": ["rockfish-mcp"],
      "env": {
        "ROCKFISH_API_KEY": "your_api_key_here",
        "ROCKFISH_API_URL": "https://api.rockfish.ai"
      }
    }
  }
}

</details>

<details> <summary>Using pip installation (requires a published package)</summary>

{
  "mcpServers": {
    "rockfish": {
      "command": "python",
      "args": ["-m", "rockfish_mcp.server"],
      "env": {
        "ROCKFISH_API_KEY": "your_api_key_here",
        "ROCKFISH_API_URL": "https://api.rockfish.ai"
      }
    }
  }
}

</details>

<details> <summary>Using docker</summary>

{
  "mcpServers": {
    "rockfish": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "ROCKFISH_API_KEY", "-e", "ROCKFISH_API_URL", "rockfish-mcp"],
      "env": {
        "ROCKFISH_API_KEY": "your_api_key_here",
        "ROCKFISH_API_URL": "https://api.rockfish.ai"
      }
    }
  }
}

</details>

Debugging

You can use the MCP inspector to debug the server from a source checkout:

npx @modelcontextprotocol/inspector uv run --directory path/to/rockfish-mcp rockfish-mcp

Or run the venv's Python directly (after uv sync):

cd path/to/rockfish-mcp
npx @modelcontextprotocol/inspector .venv/bin/python -m rockfish_mcp.server

If the server fails to start in Claude Desktop, check ~/Library/Logs/Claude/mcp-server-rockfish.log.

Development

Setup

Install with dev dependencies (the default for uv sync):

uv sync

Run tools with uv run (for example uv run pytest) or activate .venv first.

Code Formatting

isort src/rockfish_mcp/ && black src/rockfish_mcp/

Running Tests

Unit tests (no credentials required):

pytest tests/test_manta_client.py tests/test_manta_tools.py

Integration tests (requires .env with real credentials):

pytest tests/
pytest --env=.env.prod.local --html=report.html --self-contained-html

Contributing

We encourage contributions to help expand and improve rockfish-mcp. Whether you want to add new tools, enhance existing functionality, or improve documentation, your input is valuable.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Format your code with isort and black
5. Add tests if applicable
6. Submit a pull request

For examples of other MCP servers and implementation patterns, see: https://github.com/modelcontextprotocol/servers

License

rockfish-mcp is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.