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data-science-mcp

Model training, evaluation, and evolution tools for agentic ML workflows — an MCP Server + A2A Agent for the agent-utilities ecosystem, with compute delegated to the Rust epistemic-graph engine.

Official documentation

This site is the canonical reference for data-science-mcp, maintained alongside every release.

PyPI MCP Server License GitHub

Overview

data-science-mcp exposes typed, deterministic MCP tools for the full model lifecycle — load datasets, fit and cross-validate estimators, rank models on a Pareto frontier, generate interpretability suites, and fine-tune open-weight models with SFT / DPO / GRPO. It provides:

  • MLEngine — a stateful façade over the epistemic-graph Rust compute engine (fit, predict, evaluate, cross_validate, load_dataset, split_dataset). All ML compute runs in the engine over its MessagePack/UDS protocol; there is no scikit-learn compute path.
  • Action-routed MCP tools across five domains — model training, model evolution, interpretability, data management, and quantitative finance — each independently togglable to control LLM context.
  • An in-house training substrate (Wave C): a deterministic SFT/DPO/GRPO corpus and reward engine plus torch/PEFT gradient trainers, CPU-smoke-tested on a toy model.
  • A bundled Pydantic-AI A2A agent that wraps the tool surface for the Agent Control Protocol and the Agent Web UI.

Explore the documentation

  • Installation — pip, source, extras, and the prebuilt Docker image.
  • Deployment — run the MCP server and A2A agent, Docker Compose, Caddy + Technitium.
  • Usage — the MCP tools, the MLEngine Python API, and the CLI.
  • Overview — ecosystem role, enterprise readiness, and the concept registry.
  • Model Training — the SFT/DPO/GRPO corpus, reward engine, and gradient trainers.
  • Concepts — the CONCEPT:DSCI-* registry.

Quick start

pip install "data-science-mcp[all]"
data-science-mcp                 # stdio MCP server (default transport)

Run it as a network server:

data-science-mcp --transport streamable-http --host 0.0.0.0 --port 8000

See Installation and Deployment for the full matrix (PyPI extras, Docker image, all transports, the A2A agent, reverse proxy, DNS).