MCP and the Future of Cross-App AI Orchestration

You're in Claude Code, building a backend. You need a frontend to go with it. Instead of switching tools, you tell Claude: “Create the UI for this in AppsAI.” Claude calls our MCP server, and your AppsAI project updates in real-time. This is what cross-app AI orchestration looks like.

What is MCP?

Model Context Protocol (MCP) is Anthropic's open standard for connecting AI assistants to external tools and data sources. Think of it as a universal adapter—any AI that speaks MCP can interact with any tool that implements an MCP server.

Before MCP, integrating AI with software meant custom code for every combination. Claude + Slack required different integration than Claude + GitHub. The integration count grew quadratically. MCP makes it linear: implement the protocol once, connect to everything.

Why We Built an MCP Server

AppsAI is great for visual development. You see your app, you talk to the AI, changes appear on the canvas. But sometimes you're deep in a coding session elsewhere—maybe in Claude Code writing complex backend logic—and you need to modify your AppsAI project without context-switching.

With our MCP server, Claude Code can:

• Create and modify components in your AppsAI project
• Update database schemas
• Configure API endpoints
• Deploy changes to production
• Read your current project state for context

How It Works

The MCP server exposes AppsAI's capabilities as tools that Claude can call. When you tell Claude Code to modify your AppsAI project, it:

1. Authenticates using your AppsAI API key
2. Fetches your current project state
3. Determines what changes to make
4. Calls the appropriate MCP tools
5. Confirms the changes were applied

// Example: Claude Code calling AppsAI MCP tools
{
  "tool": "appsai_create_component",
  "arguments": {
    "projectId": "proj_abc123",
    "componentType": "Button",
    "props": {
      "label": "Submit",
      "variant": "primary",
      "onClick": "handleSubmit"
    },
    "parentId": "form_container"
  }
}

The Tool Catalog

Our MCP server exposes several categories of tools:

Canvas Tools

• create_component – Add a new component to the canvas
• update_component – Modify an existing component
• delete_component – Remove a component
• get_component_tree – Read the current component hierarchy

Backend Tools

• create_collection – Define a new database collection
• create_cloud_function – Add server-side logic
• get_schema – Read the current database schema

Project Tools

• deploy – Push changes to production
• get_project_status – Check deployment state
• list_projects – See all your projects

Real Workflow Example

Here's a real scenario. You're in Claude Code, writing a payment processing backend. You tell Claude:

Claude Code then:

1. Calls get_schema to understand your data model
2. Creates a new page component with a data table
3. Adds status badge components with conditional styling
4. Creates refund buttons that call your backend endpoints
5. Wires up the data fetching to your existing API

When you switch to AppsAI, the page is there, fully functional, styled consistently with your existing components.

Bidirectional Communication

MCP isn't just Claude calling AppsAI. AppsAI's Canvas AI can call out to other MCP servers too. Need to fetch data from a third-party API during development? Pull in documentation from Notion? Check your GitHub issues? If there's an MCP server for it, Canvas AI can use it.

This creates a mesh of AI capabilities. Each tool becomes a node. Each MCP connection is an edge. The AI orchestrates across all of them seamlessly.

Security Considerations

Cross-app AI raises legitimate security questions. Our approach:

• Scoped API keys – Each MCP connection uses a key with limited permissions
• Audit logging – Every MCP call is logged with full context
• Confirmation for destructive actions – Deployments and deletions require explicit approval
• Rate limiting – Prevents runaway AI from making thousands of changes

Getting Started

To connect Claude Code to your AppsAI projects:

1. Generate an API key in your AppsAI settings
2. Add the AppsAI MCP server to your Claude Code configuration
3. Start a conversation and reference your project

// claude_desktop_config.json
{
  "mcpServers": {
    "appsai": {
      "command": "npx",
      "args": ["-y", "@appsai/mcp-server"],
      "env": {
        "APPSAI_API_KEY": "your-api-key"
      }
    }
  }
}

The Bigger Picture

MCP represents a shift in how we think about AI integration. Instead of AI being a feature inside apps, AI becomes the connective tissue between apps. Your AI assistant isn't limited to one context—it can reach into any tool that speaks the protocol.

We're building toward a world where you describe what you want, and AI coordinates across all your tools to make it happen. MCP is the infrastructure that makes that possible.