OpenXAI MCP Server

OpenXAI MCP Server

A Model Context Protocol (MCP) server for OpenXAI, providing comprehensive tools for evaluating and benchmarking post hoc explanation methods in AI models.

Overview

OpenXAI is a general-purpose lightweight library that provides a comprehensive list of functions to systematically evaluate the reliability of post hoc explanation methods. This MCP server exposes OpenXAI's functionality through a standard interface that can be used with AI assistants and other MCP-compatible applications.

Features

🔍 Explanation Methods

• LIME (Local Interpretable Model-agnostic Explanations)
• SHAP (SHapley Additive exPlanations)
• Integrated Gradients
• Grad-CAM
• Guided Backpropagation

📊 Evaluation Metrics

• Faithfulness: PGI, PGU
• Stability: RIS, RRS, ROS
• Ground Truth: FA, RA, SA, SRA, RC, PRA
• Fairness: Subgroup analysis

🗂️ Datasets

• Synthetic datasets with ground truth explanations
• Real-world datasets (German Credit, COMPAS, Adult Income)
• Support for tabular, image, and text data

🤖 Pre-trained Models

• Neural Networks (ANN)
• Logistic Regression
• Random Forest
• Support Vector Machine
• XGBoost

🏆 Leaderboards

• Access to public XAI benchmarking results
• Transparent evaluation and comparison

Installation

Prerequisites

• Node.js 18+
• npm or yarn
• Python 3.7+ (for OpenXAI functionality)

Install the MCP Server

Configure with Cursor

Add the following to your Cursor settings (~/.cursor/mcp.json):

Available Tools

1. Dataset Management

list_datasets

List available datasets in the OpenXAI framework.

Parameters:

• category (optional): Filter by dataset category (synthetic, real-world, tabular, image, text, all)

Example:

load_dataset

Load a specific dataset from OpenXAI.

Parameters:

• dataset_name: Name of the dataset (e.g., german, compas, adult)
• download (optional): Whether to download if not available locally

Example:

2. Model Management

list_models

List available pre-trained models in OpenXAI.

Parameters:

• dataset_name (optional): Filter models by dataset
• model_type (optional): Filter by model type (ann, lr, rf, svm, xgb, all)

Example:

load_model

Load a pre-trained model from OpenXAI.

Parameters:

• data_name: Name of the dataset the model was trained on
• ml_model: Type of ML model (ann, lr, rf, svm, xgb)
• pretrained (optional): Whether to load pretrained model

Example:

3. Explanation Methods

list_explainers

List available explanation methods in OpenXAI.

Parameters:

• method_type (optional): Filter by method type (lime, shap, integrated_gradients, gradcam, all)

Example:

generate_explanation

Generate explanations for model predictions.

Parameters:

• method: Explanation method (lime, shap, integrated_gradients, etc.)
• data_sample: JSON string of input data to explain
• model_info: Model information object

Example:

4. Evaluation Metrics

list_metrics

List available evaluation metrics in OpenXAI.

Parameters:

• metric_type (optional): Filter by metric type (faithfulness, stability, fairness, all)

Example:

evaluate_explanation

Evaluate explanation quality using OpenXAI metrics.

Parameters:

• metric: Evaluation metric (PGI, PGU, RIS, etc.)
• explanation: JSON string of explanation to evaluate
• model_info: Model information object

Example:

5. Leaderboards

get_leaderboard

Get leaderboard results for explanation methods.

Parameters:

• dataset (optional): Dataset name
• metric (optional): Metric to sort by

Example:

6. Framework Information

get_framework_info

Get information about the OpenXAI framework.

Parameters:

• info_type (optional): Type of information (overview, features, paper, installation, quickstart)

Example:

Model Deployment Guide

🚀 Deployment Options

OpenXAI supports multiple deployment options to suit different needs and budgets:

1. Xnode (Recommended for Beginners)

• ✅ Decentralized: True decentralized deployment
• ✅ Web3 Ready: Built for blockchain integration
• ✅ No KYC: Quick setup without identity verification
• 💰 Cost: Free tier available
• 🔧 Setup: One-click deployment

Quick Start:

2. Xnode DVM (Advanced)

• ❌ Centralized: Traditional cloud deployment
• ✅ Web3 Ready: Crypto payment integration
• ✅ No KYC: Anonymous deployment
• 💰 Cost: 500 OPNX tokens
• 🔧 Performance: Higher compute resources

3. Vultr (Washington)

• ❌ Centralized: Traditional cloud provider
• ✅ Web3 Ready: Cryptocurrency payments accepted
• ✅ No KYC: Minimal verification required
• 💰 Cost: $655/month
• 🌍 Location: Washington DC, USA

4. AWS EC2 (Hong Kong)

• ❌ Centralized: Amazon Web Services
• ✅ Web3 Ready: Supports Web3 applications
• ✅ No KYC: Standard AWS verification
• 💰 Cost: $1,321/month
• 🌍 Location: Hong Kong

5. Google Cloud (NYC)

• ❌ Centralized: Google Cloud Platform
• ✅ Web3 Ready: Web3 compatible
• ✅ No KYC: Google account required
• 💰 Cost: $1,745/month
• 🌍 Location: New York City

6. Xnode One (Hardware) - Coming Soon

• ✅ Decentralized: Physical hardware nodes
• ✅ Web3 Ready: Native Web3 integration
• ✅ No KYC: Completely anonymous
• 💰 Cost: $0/month (hardware purchase required)
• 🔧 Control: Full hardware control

🔗 OpenXAI Studio Integration

Quick OpenXAI Studio Deployment

Deploy your models using OpenXAI Studio's decentralized platform:

Available Models in OpenXAI Studio

• DeepSeek R1 - Advanced reasoning model
• Code Llama - Meta's code generation model
• Gamma 2 - Google's latest model
• Llama 3.2 Vision - 90B parameter vision model
• Embedding Models - For text embeddings
• Code Models - Specialized for code generation

Deployment Process

🌐 Visit OpenXAI Studio App Store: https://studio.openxai.org/app-store

1. Connect Wallet: Web3 wallet connection for decentralized access
2. Browse App Store: Explore models in categories (General, Vision, Embedding, Code)
3. Select Model: Choose from popular models:
   • DeepSeek R1 (1.5b, 7b, 8b, 14b, 32b, 70b, 671b)
   • Code Llama (7b, 13b, 34b, 70b)
   • Qwen 2.5 (0.5b, 1.5b, 3b, 7b, 14b, 32b, 72b)
   • Llama 3.2 Vision (11b, 90b)
   • Gemma 2 (2b, 9b, 27b)
   • And many more...
4. Choose Parameters: Select model size based on your needs
5. Select Deployment Type: Choose X node or other deployment options
6. Deploy: Hit deployment button (2-5 minutes)
7. Access Deployments: Go to /deployments section
8. Login: Use provided credentials to access your deployed model

🎯 Step-by-Step Deployment

Option 1: Interactive Deployment Wizard

Option 2: Manual Configuration

1. Choose Your Provider
   # For Xnode (Free tier) npm run deploy:xnode --tier=free # For Vultr npm run deploy:vultr --region=washington # For AWS npm run deploy:aws --region=hk # For Google Cloud npm run deploy:gcp --region=nyc
2. Configure Model Settings
   { "model": { "name": "openxai-explainer", "version": "1.0.0", "framework": "openxai", "explainer": "shap", "dataset": "german" }, "deployment": { "provider": "xnode", "tier": "free", "scaling": "auto" } }
3. Set Up Authentication
   # For providers requiring authentication npm run auth:setup # Follow provider-specific login flow # Get your deployment credentials
4. Deploy and Test
   # Deploy your model npm run deploy:execute # Test your deployment npm run test:deployment # Get your endpoint URL npm run get:endpoint

🔐 Authentication & Access

User Login Flow

Similar to Hugging Face, users can easily access deployed models:

1. Visit Your Model Interface
   https://your-deployment-url/ui
2. Login Options
   • Web3 Wallet: Connect with MetaMask, WalletConnect
   • Traditional: Email/password or OAuth
   • API Key: For programmatic access
3. Model Access
   • Interactive web interface
   • API endpoints
   • SDK integration

Quick Access Example

📊 Deployment Monitoring

Real-time Metrics

Monitor your deployed models:

Cost Optimization

🔄 Switching Between Deployments

Easily switch between different deployment providers:

Usage Examples

Basic Dataset and Model Loading

Explanation Generation Workflow

Benchmarking Comparison

Deployment Workflow

OpenXAI Studio Integration Guide

When a user wants to deploy a model, here's the complete process:

🚀 Quick Start Guide

1. Visit the App Store: https://studio.openxai.org/app-store
2. Connect Wallet: Click "Connect Wallet" button
3. Browse Models: Explore categories:
   • General: qwen, deepseek-r1, llama models
   • Vision: llama-3.2-vision, qwen2-vl
   • Embedding: text-embedding models
   • Code: codelama, qwen2.5-coder
4. Select Model: Click on your preferred model
5. Choose Parameters: Select size (1.5b, 7b, 32b, etc.)
6. Configure Deployment: Choose X node (decentralized) or other options
7. Deploy: Click deploy button
8. Access: Go to /deployments and use your credentials

🔧 Using This MCP

Our MCP helps you prepare for OpenXAI Studio deployment:

Development

Running the Server

Project Structure

OpenXAI Framework

This MCP server is built on top of the OpenXAI framework:

• Website: https://open-xai.github.io/
• GitHub: https://github.com/AI4LIFE-GROUP/OpenXAI
• Paper: https://arxiv.org/abs/2206.11104

Key OpenXAI Components

1. Data Loaders: Load datasets with train/test splits
2. Model Loading: Access pre-trained models
3. Explainers: Generate explanations using various methods
4. Evaluators: Assess explanation quality
5. Leaderboards: Compare method performance

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Setup

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests (npm test)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use OpenXAI in your research, please cite:

Support

For issues and questions:

• Create an issue on GitHub
• Check the OpenXAI documentation
• Contact the OpenXAI team at openxaibench@gmail.com

Acknowledgments

• OpenXAI team for the excellent framework
• Model Context Protocol for the standard interface
• All contributors to the explainable AI community