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

# Clone the repository
git clone https://github.com/yourusername/openxai-mcp.git
cd openxai-mcp

# Install dependencies
npm install

# Install OpenXAI Python package
pip install openxai

Configure with Cursor

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

{
  "mcpServers": {
    "openxai": {
      "command": "node",
      "args": ["/path/to/openxai-mcp/index.js"],
      "env": {}
    }
  }
}

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:

List tabular datasets available in OpenXAI

`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:

Load the German Credit dataset from OpenXAI

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:

List all neural network models available in OpenXAI

`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:

Load a neural network model trained on the German Credit dataset

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:

Show me all available explanation methods in OpenXAI

`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:

Generate LIME explanations for a sample from the German Credit dataset

4. Evaluation Metrics

`list_metrics`

List available evaluation metrics in OpenXAI.

Parameters:

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

Example:

Show me all faithfulness metrics available in OpenXAI

`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:

Evaluate an explanation using the PGI metric

5. Leaderboards

`get_leaderboard`

Get leaderboard results for explanation methods.

Parameters:

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

Example:

Show me the leaderboard for the German Credit dataset

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:

Tell me about the OpenXAI framework

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:

# Deploy to Xnode
npm run deploy:xnode

# Or manually:
curl -X POST https://api.xnode.ai/deploy \
  -H "Content-Type: application/json" \
  -d '{
    "project": "openxai-mcp",
    "model": "your-model-name",
    "framework": "openxai"
  }'

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:

# 1. Setup OpenXAI Studio integration
npm run setup:openxai-studio

# 2. Connect your Web3 wallet
npm run connect:wallet

# 3. Deploy with OpenXAI Studio
npm run deploy:openxai-studio

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

Connect Wallet: Web3 wallet connection for decentralized access
Browse App Store: Explore models in categories (General, Vision, Embedding, Code)
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...
Choose Parameters: Select model size based on your needs
Select Deployment Type: Choose X node or other deployment options
Deploy: Hit deployment button (2-5 minutes)
Access Deployments: Go to /deployments section
Login: Use provided credentials to access your deployed model

🎯 Step-by-Step Deployment

Option 1: Interactive Deployment Wizard

# Run the deployment wizard
npm run deploy

# Follow the prompts:
# 1. Select deployment provider (Xnode, Vultr, AWS, etc.)
# 2. Choose your model configuration
# 3. Set up authentication (if required)
# 4. Configure scaling options
# 5. Deploy and get your endpoint URL

Option 2: Manual Configuration

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

Configure Model Settings

{
  "model": {
    "name": "openxai-explainer",
    "version": "1.0.0",
    "framework": "openxai",
    "explainer": "shap",
    "dataset": "german"
  },
  "deployment": {
    "provider": "xnode",
    "tier": "free",
    "scaling": "auto"
  }
}

Set Up Authentication

# For providers requiring authentication
npm run auth:setup

# Follow provider-specific login flow
# Get your deployment credentials

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

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

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

Quick Access Example

// JavaScript SDK
import { OpenXAIClient } from 'openxai-client';

const client = new OpenXAIClient({
  endpoint: 'https://your-deployment-url',
  apiKey: 'your-api-key'
});

// Generate explanation
const explanation = await client.explain({
  method: 'shap',
  data: [1, 0, 1, 0, 1],
  model: 'german-credit'
});

📊 Deployment Monitoring

Real-time Metrics

Monitor your deployed models:

# Check deployment status
npm run status

# View usage analytics
npm run analytics

# Monitor performance
npm run monitor

Cost Optimization

# Analyze deployment costs
npm run cost:analyze

# Optimize for budget
npm run cost:optimize

# Set up alerts
npm run alerts:setup

🔄 Switching Between Deployments

Easily switch between different deployment providers:

# List current deployments
npm run list:deployments

# Switch to different provider
npm run switch:provider --to=xnode

# Migrate between providers
npm run migrate --from=aws --to=xnode

Usage Examples

Basic Dataset and Model Loading

Load the German Credit dataset and list available models for it

Explanation Generation Workflow

1. Load the Adult Income dataset
2. Load an XGBoost model trained on this dataset
3. Generate SHAP explanations for a sample
4. Evaluate the explanations using the PGI metric

Benchmarking Comparison

1. List all explanation methods available in OpenXAI
2. Show the evaluation metrics for faithfulness
3. Get the current leaderboard for the COMPAS dataset

Deployment Workflow

1. Visit OpenXAI Studio App Store: https://studio.openxai.org/app-store
2. Connect your Web3 wallet (MetaMask, WalletConnect, etc.)
3. Browse and select your desired model from the app store
4. Choose model parameters (1.5b, 7b, 32b, etc.)
5. Select deployment type (X node for decentralized deployment)
6. Click deploy button and wait 2-5 minutes
7. Go to /deployments section to access your deployment
8. Use provided credentials to login to your deployed model
9. Start using your decentralized AI model for your projects!

OpenXAI Studio Integration Guide

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

🚀 Quick Start Guide

Visit the App Store: https://studio.openxai.org/app-store
Connect Wallet: Click "Connect Wallet" button
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
Select Model: Click on your preferred model
Choose Parameters: Select size (1.5b, 7b, 32b, etc.)
Configure Deployment: Choose X node (decentralized) or other options
Deploy: Click deploy button
Access: Go to /deployments and use your credentials

🔧 Using This MCP

Our MCP helps you prepare for OpenXAI Studio deployment:

# 1. Setup your preferences
npm run setup:openxai-studio

# 2. Connect wallet simulation
npm run connect:wallet

# 3. Get deployment guidance
npm run deploy:openxai-studio

# 4. Check deployment status
npm run status

Development

Running the Server

# Start the server
npm start

# Development mode with auto-reload
npm run dev

# Run tests
npm test

Project Structure

openxai-mcp/
├── index.js          # Main MCP server implementation
├── package.json      # Node.js dependencies
├── README.md         # This file
└── test.js          # Test suite

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

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

Contributing

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

Development Setup

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Run tests (npm test)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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:

@inproceedings{agarwal2022openxai,
  title={OpenXAI: Towards a Transparent Evaluation of Model Explanations},
  author={Agarwal, Chirag and Krishna, Satyapriya and Saxena, Eshika and Pawelczyk, Martin and Johnson, Nari and Puri, Isha and Zitnik, Marinka and Lakkaraju, Himabindu},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2022}
}

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

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

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

# Clone the repository
git clone https://github.com/yourusername/openxai-mcp.git
cd openxai-mcp

# Install dependencies
npm install

# Install OpenXAI Python package
pip install openxai

Configure with Cursor

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

{
  "mcpServers": {
    "openxai": {
      "command": "node",
      "args": ["/path/to/openxai-mcp/index.js"],
      "env": {}
    }
  }
}

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:

List tabular datasets available in OpenXAI

`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:

Load the German Credit dataset from OpenXAI

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:

List all neural network models available in OpenXAI

`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:

Load a neural network model trained on the German Credit dataset

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:

Show me all available explanation methods in OpenXAI

`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:

Generate LIME explanations for a sample from the German Credit dataset

4. Evaluation Metrics

`list_metrics`

List available evaluation metrics in OpenXAI.

Parameters:

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

Example:

Show me all faithfulness metrics available in OpenXAI

`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:

Evaluate an explanation using the PGI metric

5. Leaderboards

`get_leaderboard`

Get leaderboard results for explanation methods.

Parameters:

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

Example:

Show me the leaderboard for the German Credit dataset

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:

Tell me about the OpenXAI framework

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:

# Deploy to Xnode
npm run deploy:xnode

# Or manually:
curl -X POST https://api.xnode.ai/deploy \
  -H "Content-Type: application/json" \
  -d '{
    "project": "openxai-mcp",
    "model": "your-model-name",
    "framework": "openxai"
  }'

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:

# 1. Setup OpenXAI Studio integration
npm run setup:openxai-studio

# 2. Connect your Web3 wallet
npm run connect:wallet

# 3. Deploy with OpenXAI Studio
npm run deploy:openxai-studio

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

Connect Wallet: Web3 wallet connection for decentralized access
Browse App Store: Explore models in categories (General, Vision, Embedding, Code)
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...
Choose Parameters: Select model size based on your needs
Select Deployment Type: Choose X node or other deployment options
Deploy: Hit deployment button (2-5 minutes)
Access Deployments: Go to /deployments section
Login: Use provided credentials to access your deployed model

🎯 Step-by-Step Deployment

Option 1: Interactive Deployment Wizard

# Run the deployment wizard
npm run deploy

# Follow the prompts:
# 1. Select deployment provider (Xnode, Vultr, AWS, etc.)
# 2. Choose your model configuration
# 3. Set up authentication (if required)
# 4. Configure scaling options
# 5. Deploy and get your endpoint URL

Option 2: Manual Configuration

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

Configure Model Settings

{
  "model": {
    "name": "openxai-explainer",
    "version": "1.0.0",
    "framework": "openxai",
    "explainer": "shap",
    "dataset": "german"
  },
  "deployment": {
    "provider": "xnode",
    "tier": "free",
    "scaling": "auto"
  }
}

Set Up Authentication

# For providers requiring authentication
npm run auth:setup

# Follow provider-specific login flow
# Get your deployment credentials

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

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

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

Quick Access Example

// JavaScript SDK
import { OpenXAIClient } from 'openxai-client';

const client = new OpenXAIClient({
  endpoint: 'https://your-deployment-url',
  apiKey: 'your-api-key'
});

// Generate explanation
const explanation = await client.explain({
  method: 'shap',
  data: [1, 0, 1, 0, 1],
  model: 'german-credit'
});

📊 Deployment Monitoring

Real-time Metrics

Monitor your deployed models:

# Check deployment status
npm run status

# View usage analytics
npm run analytics

# Monitor performance
npm run monitor

Cost Optimization

# Analyze deployment costs
npm run cost:analyze

# Optimize for budget
npm run cost:optimize

# Set up alerts
npm run alerts:setup

🔄 Switching Between Deployments

Easily switch between different deployment providers:

# List current deployments
npm run list:deployments

# Switch to different provider
npm run switch:provider --to=xnode

# Migrate between providers
npm run migrate --from=aws --to=xnode

Usage Examples

Basic Dataset and Model Loading

Load the German Credit dataset and list available models for it

Explanation Generation Workflow

1. Load the Adult Income dataset
2. Load an XGBoost model trained on this dataset
3. Generate SHAP explanations for a sample
4. Evaluate the explanations using the PGI metric

Benchmarking Comparison

1. List all explanation methods available in OpenXAI
2. Show the evaluation metrics for faithfulness
3. Get the current leaderboard for the COMPAS dataset

Deployment Workflow

1. Visit OpenXAI Studio App Store: https://studio.openxai.org/app-store
2. Connect your Web3 wallet (MetaMask, WalletConnect, etc.)
3. Browse and select your desired model from the app store
4. Choose model parameters (1.5b, 7b, 32b, etc.)
5. Select deployment type (X node for decentralized deployment)
6. Click deploy button and wait 2-5 minutes
7. Go to /deployments section to access your deployment
8. Use provided credentials to login to your deployed model
9. Start using your decentralized AI model for your projects!

OpenXAI Studio Integration Guide

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

🚀 Quick Start Guide

Visit the App Store: https://studio.openxai.org/app-store
Connect Wallet: Click "Connect Wallet" button
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
Select Model: Click on your preferred model
Choose Parameters: Select size (1.5b, 7b, 32b, etc.)
Configure Deployment: Choose X node (decentralized) or other options
Deploy: Click deploy button
Access: Go to /deployments and use your credentials

🔧 Using This MCP

Our MCP helps you prepare for OpenXAI Studio deployment:

# 1. Setup your preferences
npm run setup:openxai-studio

# 2. Connect wallet simulation
npm run connect:wallet

# 3. Get deployment guidance
npm run deploy:openxai-studio

# 4. Check deployment status
npm run status

Development

Running the Server

# Start the server
npm start

# Development mode with auto-reload
npm run dev

# Run tests
npm test

Project Structure

openxai-mcp/
├── index.js          # Main MCP server implementation
├── package.json      # Node.js dependencies
├── README.md         # This file
└── test.js          # Test suite

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

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

Contributing

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

Development Setup

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Run tests (npm test)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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:

@inproceedings{agarwal2022openxai,
  title={OpenXAI: Towards a Transparent Evaluation of Model Explanations},
  author={Agarwal, Chirag and Krishna, Satyapriya and Saxena, Eshika and Pawelczyk, Martin and Johnson, Nari and Puri, Isha and Zitnik, Marinka and Lakkaraju, Himabindu},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2022}
}

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

OpenXAI MCP Server

OpenXAI MCP Server

Overview

Features

Installation

Prerequisites

Install the MCP Server

Configure with Cursor

Available Tools

1. Dataset Management

list_datasets

load_dataset

2. Model Management

list_models

load_model

3. Explanation Methods

list_explainers

generate_explanation

4. Evaluation Metrics

list_metrics

evaluate_explanation

5. Leaderboards

get_leaderboard

6. Framework Information

get_framework_info

Model Deployment Guide

🚀 Deployment Options

1. Xnode (Recommended for Beginners)

2. Xnode DVM (Advanced)

3. Vultr (Washington)

4. AWS EC2 (Hong Kong)

5. Google Cloud (NYC)

6. Xnode One (Hardware) - Coming Soon

🔗 OpenXAI Studio Integration

Quick OpenXAI Studio Deployment

Available Models in OpenXAI Studio

Deployment Process

🎯 Step-by-Step Deployment

Option 1: Interactive Deployment Wizard

Option 2: Manual Configuration

🔐 Authentication & Access

User Login Flow

Quick Access Example

📊 Deployment Monitoring

Real-time Metrics

Cost Optimization

🔄 Switching Between Deployments

Usage Examples

Basic Dataset and Model Loading

Explanation Generation Workflow

Benchmarking Comparison

Deployment Workflow

OpenXAI Studio Integration Guide

🚀 Quick Start Guide

🔧 Using This MCP

Development

Running the Server

Project Structure

OpenXAI Framework

Key OpenXAI Components

Contributing

Development Setup

License

Citation

Support

Acknowledgments

OpenXAI MCP Server

Overview

Features

Installation

Prerequisites

Install the MCP Server

Configure with Cursor

Available Tools

1. Dataset Management

list_datasets

load_dataset

2. Model Management

list_models

load_model

`list_datasets`

`load_dataset`

`list_models`

`load_model`

`list_explainers`

`generate_explanation`

`list_metrics`

`evaluate_explanation`

`get_leaderboard`

`get_framework_info`

`list_datasets`

`load_dataset`

`list_models`

`load_model`

`list_explainers`

`generate_explanation`

`list_metrics`

`evaluate_explanation`

`get_leaderboard`

`get_framework_info`