ViewMax Studio MCP Server

An MCP (Model Context Protocol) server that enables Claude and other LLM clients to generate AI video prompts with narrative scripts using the ViewMax Studio API.

Features

• 7 Video Formats: Shoppable Video, Viral Hook, Trending, Meme, POV & Roleplay, Reaction, Storytelling
• 11 AI Models: Seedance, Kling, Grok, Runway, Gemini, Veo with automatic model selection
• Prompt Generation: Create engaging video prompts tailored to your format
• Script Generation: Generate narrative scripts aligned with your prompt
• Task Tracking: Monitor video generation progress with task IDs
• Character Validation: Enforce 2000-character limits for prompts and scripts
• HTTP Deployment: Built with FastMCP for remote HTTP access

Quick Start

1. Setup

# Clone the repository
git clone <your-repo-url>
cd viewmax-mcp

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create .env file
cp .env.example .env
# Edit .env and add your ViewMax API key

2. Local Development

# Make sure you're in the virtual environment with dependencies installed
python app.py

The server will start at http://localhost:8000 and be accessible at http://localhost:8000/mcp

3. Using in Claude Desktop

1. Create/update your claude_desktop_config.json:

   • macOS/Linux: ~/.config/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Add the MCP server configuration:

{
  "mcpServers": {
    "viewmax": {
      "command": "python",
      "args": ["<path-to>/app.py"],
      "env": {
        "VIEWMAX_API_KEY": "your-api-key-here"
      }
    }
  }
}

3. Restart Claude Desktop and the tools should be available

4. Using in Cowork

1. In Cowork settings, go to Connectors
2. Add a new connector with type "MCP"
3. Point it to your running server at http://localhost:8000 (for local testing)
4. Or use the deployed URL for remote access

Deployment to Railway

Prerequisites

• Railway account (free tier available at railway.app)
• GitHub repository with your code
• ViewMax API key set as environment variable

Steps

1. Push to GitHub

   git add .
   git commit -m "Initial ViewMax MCP server"
   git push origin main

2. Deploy on Railway

   • Connect your GitHub account to Railway
   • Create new project → Deploy from GitHub
   • Select your repository
   • Railway will auto-detect the Procfile and run the server

3. Set Environment Variables

   • In Railway dashboard, go to Variables
   • Add VIEWMAX_API_KEY with your actual API key
   • Railway automatically provides PORT environment variable

4. Access Your Server

   • Railway provides a public domain like: https://viexmaxmcp-production.up.railway.app
   • MCP endpoint: https://viexmaxmcp-production.up.railway.app/mcp

Adding to Claude via Connector

In Cowork Connectors, use the deployed URL:

https://viexmaxmcp-production.up.railway.app

API Tools

1. viewmax_generate_prompt_and_script

Generates both a video prompt and narrative script based on your topic and format.

Inputs:

• topic: The subject matter for the video
• format: One of 7 video formats (shoppable_video, viral_hook, trending, meme, pov_roleplay, reaction, storytelling)
• duration: Video length in seconds (15-120, default: 30)
• style: Optional style/mood (e.g., "cinematic", "casual")
• tone: Optional voice tone (e.g., "professional", "energetic")

Outputs:

• Generated prompt (max 2000 characters)
• Generated script (max 2000 characters)
• Selected model and its cost
• Character counts and ready-to-submit status

2. viewmax_submit_video

Submits a video generation request to ViewMax API.

Inputs:

• prompt: Your video prompt text
• script: Narrative script for the video
• format: Video format type
• model: AI model to use (from the 11 available)

Outputs:

• Task ID for tracking
• Submission status
• Model used and format

3. viewmax_check_task_status

Checks the progress of a submitted video generation task.

Inputs:

• task_id: The ID returned from video submission

Outputs:

• Current status (generating, completed, failed, etc.)
• Progress percentage
• Estimated completion time
• Video URL (when ready)

Available Models

Model	Cost (credits/min)	Quality	Best For
Seedance 1.5 Pro	8.0	High	General purpose
Seedance 2.0	10.0	Very High	Premium quality
Seedance 2.0 Fast	6.0	High	Quick turnaround
Kling 2.6	9.0	Very High	Complex scenes
Grok Imagine	7.0	High	Creative content
Runway	8.5	Very High	Professional videos
Gemini Omni Flash	5.0	Medium-High	Budget-friendly
Veo 3.1	12.0	Excellent	Premium production
Veo 3.1 Fast	9.0	Very High	Fast premium
Veo 3.1 Lite	4.5	Medium-High	Quick generation

Architecture

• Framework: FastMCP - Python framework for building MCP servers
• Transport: HTTP with ASGI (Starlette/Uvicorn)
• API Client: httpx (async HTTP client)
• Validation: Pydantic models for type safety
• Deployment: Railway with Procfile automation

Key Implementation Details

1. HTTP Transport: Uses mcp.http_app() to expose the server as an ASGI application, accessible via HTTP endpoints
2. Automatic Model Selection: Recommends optimal model based on video format
3. Progress Reporting: Tools report progress in real-time using MCP context
4. Error Handling: Comprehensive error messages for validation and API failures
5. Async/Await: Non-blocking operations for API calls and progress updates

Troubleshooting

"Connection refused" when running locally

• Ensure the server is running: python app.py
• Check that port 8000 is not already in use
• Try accessing http://localhost:8000/mcp in your browser

"API Key invalid" error

• Verify your VIEWMAX_API_KEY in the .env file
• Ensure the key hasn't expired
• Check the .env file is being loaded

MCP server not appearing in Claude

• Restart Claude Desktop after updating configuration
• Check the MCP server logs for startup errors
• Verify the endpoint URL is accessible

Development

Testing Locally

# Run the server
python app.py

# In another terminal, test with curl
curl -X POST http://localhost:8000/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "resources/list",
    "params": {}
  }'

Building on the Existing Code

To extend this MCP server:

1. Add new tools by using the @mcp.tool decorator
2. Define new Pydantic models for tool inputs
3. Use ctx.report_progress() for long-running tasks
4. Add API calls to ViewMax endpoints as needed

License

MIT License - See LICENSE file for details

Support

For issues or questions:

1. Check the README troubleshooting section
2. Review the FastMCP documentation: https://gofastmcp.com
3. Check ViewMax API documentation for API-specific issues