OpenXAI MCP Server

Instructions

Implementation Reference

• index.js:1003-1230 (handler)

  The handler function that provides step-by-step deployment guides for different deployment types in OpenXAI Studio, returning formatted text content based on the input deployment_type.
  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:236-245 (schema)

  The input schema defining the deployment_type parameter for the get_deployment_guide tool.
  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)

  Registration of the get_deployment_guide tool in the listTools response, including name, description, and input schema.
  { 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)

  Dispatch case in the CallToolRequest handler that routes calls to the getDeploymentGuide method.
  case 'get_deployment_guide': return await this.getDeploymentGuide(args.deployment_type || 'quick_start');