OpenXAI MCP Server

Instructions

Implementation Reference

• index.js:1003-1230 (handler)

  Executes the tool by selecting and returning a predefined markdown guide for OpenXAI Studio model deployment based on the deployment_type parameter.

    async getDeploymentGuide(deploymentType) {
      const guides = {
        quick_start: `🚀 OpenXAI Studio Quick Start Guide
  
  To deploy your AI model using OpenXAI Studio's decentralized platform:
  
  1. 🌐 **Visit OpenXAI Studio App Store**
     https://studio.openxai.org/app-store
  
  2. 🔗 **Connect Your Web3 Wallet**
     - Click "Connect Wallet" button
     - Choose MetaMask, WalletConnect, or other wallets
     - Approve the connection
  
  3. 🤖 **Select Your Model**
     Browse categories and choose from:
     • General: qwen, deepseek-r1, llama models
     • Vision: llama-3.2-vision, qwen2-vl
     • Embedding: text-embedding models
     • Code: codelama, qwen2.5-coder
  
  4. ⚙️ **Choose Parameters**
     Select model size: 1.5b, 7b, 32b, 70b, etc.
  
  5. 🚀 **Select Deployment Type**
     Choose X node for decentralized deployment
  
  6. 🔥 **Deploy**
     Click deploy button and wait 2-5 minutes
  
  7. 📊 **Access Your Deployment**
     Go to /deployments section
  
  8. 🔑 **Login & Use**
     Use provided credentials to access your deployed model
  
  🎯 **Ready to start?** Visit https://studio.openxai.org/app-store now!`,
  
        detailed: `📋 OpenXAI Studio Detailed Deployment Guide
  
  **Pre-requisites:**
  - Web3 wallet (MetaMask, WalletConnect, etc.)
  - Sufficient crypto balance for deployment costs
  - Clear understanding of your model requirements
  
  **Step-by-Step Process:**
  
  **Phase 1: Preparation**
  1. 📱 Install and setup your Web3 wallet
  2. 🔐 Secure your wallet with strong passwords
  3. 💰 Ensure adequate balance for deployment
  
  **Phase 2: Model Selection**
  1. 🌐 Navigate to https://studio.openxai.org/app-store
  2. 🔍 Browse available models by category:
     - **General Models**: Multi-purpose language models
     - **Vision Models**: Image and video processing
     - **Embedding Models**: Text similarity and search
     - **Code Models**: Programming and code generation
  
  3. 📊 Compare model specifications:
     - Parameter counts (1.5b, 7b, 32b, 70b, etc.)
     - Memory requirements
     - Processing capabilities
     - Cost implications
  
  **Phase 3: Deployment Configuration**
  1. ⚙️ Select resource requirements:
     - CPU cores needed
     - RAM allocation
     - Storage requirements
     - Network bandwidth
  
  2. 🌐 Choose deployment type:
     - **X Node**: Decentralized deployment (recommended)
     - **Traditional**: Centralized deployment options
  
  3. 💳 Select subscription model:
     - Side Later: Pay-as-you-go
     - ERC 4337: Subscription service
     - Model Ownership: Full control
     - Fractionalized AI: Shared ownership
  
  **Phase 4: Deployment Execution**
  1. 🚀 Review configuration summary
  2. 🔥 Click deploy button
  3. ⏳ Wait 2-5 minutes for deployment
  4. 📊 Monitor deployment progress
  
  **Phase 5: Access & Management**
  1. 🔑 Receive deployment credentials
  2. 📊 Access /deployments section
  3. 🔐 Login with provided credentials
  4. 🎯 Start using your deployed model
  
  **Troubleshooting:**
  - Wallet connection issues
  - Deployment failures
  - Access problems
  - Performance optimization`,
  
        app_store: `🛒 OpenXAI Studio App Store Guide
  
  **App Store URL:** https://studio.openxai.org/app-store
  
  **Navigation:**
  - **Categories**: General, Vision, Embedding, Code
  - **Popular Models**: Featured and trending models
  - **Search**: Find specific models quickly
  - **Filters**: Sort by parameters, popularity, cost
  
  **Available Models:**
  
  **📚 General Models:**
  - qwen: Versatile language model
  - deepseek-r1: Advanced reasoning capabilities
  - llama models: Meta's flagship models
  - gemma: Google's efficient models
  
  **👁️ Vision Models:**
  - llama-3.2-vision: Multi-modal understanding
  - qwen2-vl: Vision-language processing
  - Advanced image recognition models
  
  **🔍 Embedding Models:**
  - text-embedding-3-small: Efficient embeddings
  - text-embedding-3-large: High-quality embeddings
  - Specialized semantic search models
  
  **💻 Code Models:**
  - codelama: Meta's code generation
  - qwen2.5-coder: Advanced coding assistant
  - Programming language specialists
  
  **Model Selection Tips:**
  1. 🎯 Match model to your use case
  2. 📊 Consider parameter count vs. performance
  3. 💰 Balance cost with capabilities
  4. 🔄 Test with smaller models first
  5. 📈 Scale up based on results
  
  **Deployment Options:**
  - **X Node**: Decentralized, cost-effective
  - **Standard**: Traditional cloud deployment
  - **Custom**: Specialized configurations
  
  **Getting Started:**
  1. Visit the app store
  2. Connect your wallet
  3. Browse models
  4. Select and deploy
  5. Access via /deployments`,
  
        troubleshooting: `🔧 OpenXAI Studio Troubleshooting
  
  **Common Issues & Solutions:**
  
  **🔗 Wallet Connection Problems:**
  - **Issue**: Wallet won't connect
  - **Solution**: 
    1. Refresh the page
    2. Clear browser cache
    3. Try different browser
    4. Check wallet extension
  
  **🚀 Deployment Failures:**
  - **Issue**: Deployment times out
  - **Solution**:
    1. Check network connectivity
    2. Verify sufficient wallet balance
    3. Try smaller model first
    4. Contact support if persistent
  
  **🔐 Access Issues:**
  - **Issue**: Can't access deployed model
  - **Solution**:
    1. Check credentials are correct
    2. Wait for deployment to complete
    3. Try different browser
    4. Clear cookies and cache
  
  **⚡ Performance Problems:**
  - **Issue**: Model runs slowly
  - **Solution**:
    1. Upgrade to higher-parameter model
    2. Increase resource allocation
    3. Optimize input data
    4. Consider X node deployment
  
  **💰 Cost Issues:**
  - **Issue**: Unexpected charges
  - **Solution**:
    1. Review subscription model
    2. Monitor usage in /deployments
    3. Set up cost alerts
    4. Consider different deployment type
  
  **📊 Monitoring Issues:**
  - **Issue**: Can't see deployment status
  - **Solution**:
    1. Refresh /deployments page
    2. Check wallet connection
    3. Verify deployment ID
    4. Contact support
  
  **🆘 Getting Help:**
  - Documentation: https://studio.openxai.org/docs
  - Community: Discord/Telegram support
  - Support: Contact through app
  - Status: Check system status page
  
  **Prevention Tips:**
  1. 🔐 Keep wallet secure
  2. 📊 Monitor usage regularly
  3. 💰 Set spending limits
  4. 🔄 Test small deployments first
  5. 📚 Read documentation thoroughly`
      };
  
      return {
        content: [
          {
            type: 'text',
            text: guides[deploymentType] || guides.quick_start
          }
        ]
      };
    }

• index.js:235-245 (schema)

  Defines the input schema for the tool, specifying an optional deployment_type parameter with valid enum values.

  inputSchema: {
    type: 'object',
    properties: {
      deployment_type: {
        type: 'string',
        description: 'Type of deployment guidance needed',
        enum: ['quick_start', 'detailed', 'app_store', 'troubleshooting']
      }
    },
    required: []
  }

• index.js:232-246 (registration)

  Registers the get_deployment_guide tool in the MCP server's tool list.

  {
    name: 'get_deployment_guide',
    description: 'Get step-by-step guidance for deploying models using OpenXAI Studio',
    inputSchema: {
      type: 'object',
      properties: {
        deployment_type: {
          type: 'string',
          description: 'Type of deployment guidance needed',
          enum: ['quick_start', 'detailed', 'app_store', 'troubleshooting']
        }
      },
      required: []
    }
  }

• index.js:285-286 (registration)

  Registers the tool handler dispatch in the CallToolRequestHandler switch statement.

  case 'get_deployment_guide':
    return await this.getDeploymentGuide(args.deployment_type || 'quick_start');