MCP Ml Lab

Name: MCP Ml Lab
Author: rohithraju-ops

by rohithraju-opsversion 0.1.0i★ –iRuns on your computer

Registry nameio.github.rohithraju-ops/mcp-ml-lab

Install

Overview

Description from the maker (original English)

Run end-to-end ML experiments from natural language (XGBoost, LightGBM, Optuna).

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new↓ 24/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "ml-lab": {
      "command": "uvx",
      "args": [
        "mcp-ml-lab"
      ]
    }
  }
}

Run this in your terminal

claude mcp add ml-lab -- uvx mcp-ml-lab

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "ml-lab": {
      "command": "uvx",
      "args": [
        "mcp-ml-lab"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "ml-lab": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mcp-ml-lab"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account rohithraju-ops, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 18475 servers in this category
Published to the registry more recently than 9% of the other 18475 servers in this category
Basic checks equal to or better than 100% of the other 18475 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is MCP Ml Lab safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Tethered Trust

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

mcp-ml-lab

Let AI agents run real ML experiments end-to-end.

An MCP server that gives Claude (or any MCP-aware AI agent) the ability to profile a CSV, define an ML task, tune XGBoost and LightGBM with Optuna, and produce a markdown report with feature importance — all from natural language.

Why this exists

The existing ML-related MCP servers wrap MLflow, ZenML, or Weights & Biases and expose them as read-only — agents can browse experiment history but can't actually *run* anything. mcp-ml-lab fills the gap: it lets agents execute the full experimentation loop from a user's natural-language request.

A user typing "train a model on titanic.csv to predict survival" should not need to know what XGBoost is, what cross-validation is, or how to write a hyperparameter search. The agent handles all of that — mcp-ml-lab is the tools layer that makes it possible.

Quick start

pip install mcp-ml-lab

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

"mcpServers": { "ml-lab": { "command": "mcp-ml-lab" } } }

Restart Claude Desktop. The five tools below are now available.

Example queries

Try these in Claude Desktop with mcp-ml-lab connected:

*"Profile this CSV and tell me if there's class imbalance"*
*"Compare XGBoost and LightGBM on titanic.csv with 60 seconds of tuning"*
*"Show me the top 10 features the winning model used"*
*"How did my last three experiments on the wine dataset compare?"*

Tools

Tool	What it does
`inspect_data`	Profile a CSV — shape, dtypes, nulls, summary stats, class balance
`define_task`	Register an ML task (CSV + target + classification/regression)
`run_experiment`	Train one or more models, optionally tuning with Optuna
`get_results`	Markdown report with metrics, hyperparameters, feature importance
`compare_runs`	Side-by-side comparison of multiple experiments

Each tool's full signature is in its docstring; they self-document to the LLM.

How it works

Claude Desktop  ───MCP/stdio───  mcp-ml-lab server

│ ├── data.py CSV loading, schema inference, preprocessor ├── trainers/ Pluggable XGBoost + LightGBM adapters ├── search.py Stratified CV + Optuna TPE tuning ├── metrics.py Accuracy, F1, AUC, log loss ├── storage.py SQLite via SQLAlchemy 2.0 └── reporting.py Markdown report generation

All experiments and trials are persisted to ~/.mcp-ml-lab/store.db so an agent can refer back to runs across sessions.

Full design notes in ARCHITECTURE.md.

Roadmap

v0.1.0 ships classification with XGBoost and LightGBM. Planned for v0.2.0+:

Regression tasks
Time series forecasting (sktime / darts integration)
Deep learning baselines (pytorch-tabular)
Optuna multi-objective search (accuracy × latency × model size)
Persisted model artifacts with Docker reproducibility
Permutation feature importance (bias-free alternative to gain importance)
Notebook export — emit a Jupyter notebook that reproduces the winning run

Issues and PRs welcome.

Development

git clone https://github.com/rohithraju-ops/mcp-ml-lab.git

cd mcp-ml-lab python -m venv .venv && source .venv/bin/activate pip install -e ".[dev]" pytest -v

Local debugging is easiest with the MCP Inspector:

npx @modelcontextprotocol/inspector mcp-ml-lab

License

MIT.

MCP Ml Lab

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Tethered Trust

README from the maker

mcp-ml-lab

Why this exists

Quick start

Example queries

Tools

How it works

Roadmap

Development

License