WEKA-MCP
A machine-learning workbench your model can actually use.
WEKA-MCP puts the WEKA machine-learning workbench behind 46 typed Model Context Protocol tools. A language model can load a dataset, train a model, make predictions, and inspect where it fails, while WEKA remains responsible for the computation.
MCP on one side, WEKA on the other
The project separates the translation layer from the machine-learning workbench. The MCP server forwards typed tool calls to WEKA, so the model can explain results without quietly becoming the place where those results are calculated.
weka-mcp
A TypeScript MCP server that speaks HTTP and stdio, turning each tool call into a WEKA API request.
weka-api
A Java service wrapping WEKA, where training, evaluation, clustering, and diagnostics actually happen.
From data to diagnosis
The toolset follows the way a real analysis unfolds: understand the data, transform it, train a model, evaluate it, and investigate the errors.
Why the boundary matters
Language models are good at explaining results, but they should not invent the statistics they explain. By keeping the computation in WEKA and exposing it through typed tools, every result can be traced back to a real dataset and a real algorithm.
Built on WEKA from the University of Waikato and served over the Model Context Protocol.