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

Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run rockfish-mcp.

Using pip

pip install rockfish-mcp

After installation, you can run it as a script using:

python -m rockfish_mcp.server

From source

git clone https://github.com/Rockfish-Data/rockfish-mcp.git
cd rockfish-mcp
python3.11 -m venv .venv
source .venv/bin/activate

2. Install dependencies (choose one method):

Method A: Install with dev tools (recommended for contributors):

pip install -e ".[dev]" --find-links https://packages.rockfish.ai

Method B: Install from requirements.txt (exact locked versions):

pip install -r requirements.txt

Method C: Install runtime only (for production):

pip install -e . --find-links https://packages.rockfish.ai

3. 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> <summary>Using uvx</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</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>

Usage with VS Code

For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

Optionally, you can add it to a file called .vscode/mcp.json in your workspace.

Note that the mcp key is needed when using the mcp.json file.

<details> <summary>Using uvx</summary>

{
  "mcp": {
    "servers": {
      "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</summary>

{
  "mcp": {
    "servers": {
      "rockfish": {
        "command": "python",
        "args": ["-m", "rockfish_mcp.server"],
        "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. For uvx installations:

npx @modelcontextprotocol/inspector uvx rockfish-mcp

Or if you've installed the package in a specific directory or are developing on it:

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

Development

Setup

Install with dev dependencies:

pip install -e ".[dev]" --find-links https://packages.rockfish.ai

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.