MCP 服务器

HistGradientBoostingClassifier MCP Server

Provides tools for training, managing, and making predictions with scikit-learn's HistGradientBoostingClassifier models. It allows users to handle model lifecycles, including creation, evaluation, and serialization, via the Model Context Protocol.

README

HistGradientBoostingClassifier MCP Server

A Model Context Protocol (MCP) server that provides tools for training, predicting, and managing sklearn's HistGradientBoostingClassifier models.

Features

This MCP server exposes the following tools:

create_classifier: Create a new HistGradientBoostingClassifier with custom parameters
train_model: Train a classifier on provided data
predict: Make class predictions on new data
predict_proba: Get class probabilities for predictions
score_model: Evaluate model accuracy on test data
get_model_info: Get detailed information about a model
list_models: List all available models
delete_model: Remove a model from memory
save_model: Serialize a model to base64 string
load_model: Load a model from serialized string

Installation

pip install -r requirements.txt

Local Development

Run the server locally:

uvrun --with mcp server.py

The server will start on http://localhost:8000 by default.

Railway Deployment

Prerequisites

A Railway account (sign up at https://railway.app)
Railway CLI installed (optional, can use web interface)
Git repository with your code (GitHub, GitLab, or Bitbucket)

Deploy via Railway Web Interface

Go to https://railway.app and create a new project
Click "New Project" → "Deploy from GitHub repo"
Select your repository containing this MCP server
Railway will automatically detect the Python project and use the Procfile
The server will be deployed and you'll get a public URL (e.g., https://your-app.railway.app)

Deploy via Railway CLI

# Install Railway CLI
npm i -g @railway/cli

# Login to Railway
railway login

# Initialize project (in your project directory)
railway init

# Link to existing project or create new one
railway link

# Deploy
railway up

Environment Variables

No environment variables are required for basic operation. Railway automatically provides:

PORT: The port your application should listen on
The server automatically binds to 0.0.0.0 to accept external connections

Verifying Deployment

Once deployed, your MCP server will be available at your Railway URL. You can test it by:

Visiting https://your-app.railway.app in a browser (should show MCP server info or 404, which is normal)
Using the MCP Inspector: npx -y @modelcontextprotocol/inspector and connecting to your Railway URL
Connecting from an MCP client using the streamable-http transport

Current Deployment URL: https://web-production-a620a.up.railway.app

Usage

Once deployed, the MCP server will be accessible at your Railway URL. You can connect to it using any MCP-compatible client.

Example: Using with Claude Desktop

Add to your Claude Desktop MCP configuration (~/Library/Application Support/Claude/claude_desktop_config.json on Mac):

{
  "mcpServers": {
    "histgradientboosting": {
      "url": "https://web-production-a620a.up.railway.app",
      "transport": "streamable-http"
    }
  }
}

Example API Calls

The server exposes tools that can be called via MCP protocol. Here's what each tool does:

Create a classifier:

create_classifier(
    model_id="my_model",
    learning_rate=0.1,
    max_iter=100,
    max_leaf_nodes=31
)

Train the model:

train_model(
    model_id="my_model",
    X=[[1, 2], [3, 4], [5, 6]],
    y=[0, 1, 0]
)

Make predictions:

predict(
    model_id="my_model",
    X=[[2, 3], [4, 5]]
)

Get probabilities:

predict_proba(
    model_id="my_model",
    X=[[2, 3], [4, 5]]
)

Model Storage

Currently, models are stored in-memory. This means:

Models persist only during the server's lifetime
Restarting the server will lose all models
For production use, consider implementing persistent storage (database, file system, or cloud storage)