get_deployment_guide

Get step-by-step guidance for deploying AI models through OpenXAI Studio, covering quick starts, detailed setups, app store integration, and troubleshooting.

Instructions

Get step-by-step guidance for deploying models using OpenXAI Studio

Input Schema

TableJSON Schema

Name	Required	Description	Default
`deployment_type`	No	Type of deployment guidance needed

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');
```

OpenXAI MCP Server