Provides access to the Rockfish machine learning platform API, enabling management of databases, worker sets, ML workflows, models, projects, and datasets through Python-based tools.
Rockfish MCP Server
A Model Context Protocol (MCP) server that provides access to the Rockfish API, enabling AI assistants to interact with Rockfish's machine learning platform.
Features
This MCP server provides tools for the following Rockfish resources:
- Databases: Create, list, update, and delete databases
- Worker Sets: Manage worker sets for distributed processing
- Workflows: Create and manage ML workflows
- Models: Upload, list, and manage ML models
- Projects: Organize and manage projects
- Datasets: Create and manage datasets
Installation
- Clone the repository:
- Install dependencies:
- Set up environment variables:
Configuration
Create a .env
file with your Rockfish API credentials:
Usage
Run the MCP server:
Or use the console script:
Claude Desktop Setup
To use this MCP server with Claude Desktop:
- Complete the installation steps above (clone, install dependencies, set up .env file)
- Find your Claude Desktop configuration directory:
- macOS:
~/Library/Application Support/Claude/
- Windows:
%APPDATA%\Claude\
- Linux:
~/.config/Claude/
- macOS:
- Create or edit the
claude_desktop_config.json
file in that directory:
- Update the paths in the configuration:
- Replace
/path/to/your/project/.venv/bin/python
with the actual path to your Python executable - Replace
your_api_key_here
with your actual Rockfish API key - Adjust
ROCKFISH_BASE_URL
if you're using a different endpoint
- Replace
- Get the correct Python path by running this command in your project directory:
- Example configuration (replace with your actual paths and API key):
- Restart Claude Desktop after making these changes
- Test the connection by asking Claude to list your Rockfish databases or projects
MCP Inspector Setup
The MCP Inspector is a debugging tool that helps you test your MCP server before connecting it to Claude Desktop.
Installation
Usage
- Start the MCP Inspector:
- Or create a test script for easier repeated testing:
Make it executable and run:
- The Inspector will open in your browser and show:
- Available tools (should show all 22 Rockfish tools)
- Tool schemas and descriptions
- Interactive tool testing interface
- Test your tools by:
- Selecting a tool from the list (e.g.,
list_databases
) - Filling in required parameters
- Clicking "Call Tool" to test the API call
- Viewing the response
- Selecting a tool from the list (e.g.,
Useful Tools to Test First
list_databases
- Simple GET request with no parameterslist_projects
- Another simple list operationget_database
- Test with a database ID from the listcreate_database
- Test creating a new resource
Troubleshooting
- MCP server not appearing: Check that the Python path is correct and the virtual environment is activated
- Authentication errors: Verify your
ROCKFISH_API_KEY
is correct - Connection issues: Confirm your
ROCKFISH_BASE_URL
is accessible - Path issues on Windows: Use forward slashes or escaped backslashes in JSON paths
Available Tools
Database Tools
list_databases
: List all databasescreate_database
: Create a new databaseget_database
: Get a specific database by IDupdate_database
: Update a databasedelete_database
: Delete a database
Worker Set Tools
list_worker_sets
: List all worker setscreate_worker_set
: Create a new worker setget_worker_set
: Get a specific worker set by IDdelete_worker_set
: Delete a worker set
Workflow Tools
list_workflows
: List all workflowscreate_workflow
: Create and run a new workflowget_workflow
: Get a specific workflow by IDupdate_workflow
: Update a workflow
Model Tools
list_models
: List all modelsupload_model
: Upload a new modelget_model
: Get a specific model by IDdelete_model
: Delete a model
Project Tools
list_projects
: List all projectscreate_project
: Create a new projectget_project
: Get a specific project by IDupdate_project
: Update a project
Dataset Tools
list_datasets
: List all datasetscreate_dataset
: Create a new datasetget_dataset
: Get a specific dataset by IDupdate_dataset
: Update a datasetdelete_dataset
: Delete a dataset
Development
To contribute to this project:
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests if applicable
- Submit a pull request
License
MIT License
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Enables AI assistants to interact with Rockfish's machine learning platform through comprehensive API access. Supports managing databases, worker sets, workflows, models, projects, and datasets for ML operations.
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