What is MCP?
The Model Context Protocol (MCP) is an open standard that allows AI models to securely connect to external tools and data sources. With the Control Plane MCP Server, AI assistants can:Manage Resources
Create and configure GVCs, workloads, and more
Deploy Applications
Build images and deploy applications across global locations
Query Infrastructure
Get real-time information about your Control Plane resources
Automate Workflows
Streamline DevOps tasks through conversational AI
Quick Start
1
Create a Service Account
Create a service account with the appropriate permissions for your use case. Generate an API key that
will be used for authentication.
2
Configure Your Client
Add the Control Plane MCP Server to your AI assistant or coding tool. See the setup guides below for tool-specific instructions.
3
Start Building
Begin interacting with Control Plane through natural language commands.
MCP Server Endpoint
https://mcp.cpln.io/mcpAuthentication
The MCP Server requires authentication via a service account token. Include the token in theAuthorization header:
Compatible Tools
The Control Plane MCP Server works with any AI assistant or development tool that supports remote MCP servers. Below are setup guides for popular tools:Claude Code
Claude Code CLI
Codex
OpenAI Codex
Cursor
Cursor IDE
VS Code
Visual Studio Code
Antigravity
Google Antigravity IDE
Don’t see your tool listed? Any client that supports remote MCP servers can connect to the Control Plane MCP Server using the standard configuration format. Check your tool’s documentation for remote MCP server setup instructions.
Service Account Permissions
The MCP Server respects all Control Plane policies and permissions. Your service account’s capabilities depend on the groups and policies assigned to it.Built-in Groups
Control Plane provides two built-in groups for common permission patterns:| Group | Permissions | Use Case |
|---|---|---|
| viewers | view on all resources | Read-only exploration, querying infrastructure status |
| superusers | manage on all resources | Full automation, creating and modifying all resources |
Custom Permissions
For more granular control, you can:- Create custom groups with specific members
- Define policies that grant targeted permissions
- Assign your service account to groups that match your use case
Recommended Approaches
| Use Case | Recommended Setup |
|---|---|
| Quick exploration | Add service account to viewers group |
| Full development access | Add service account to superusers group |
| Production automation | Create custom group with specific policies for required resources |
| Team-specific access | Create group per team with policies scoped to their GVCs |
Best Practices
Start with Context
Always set your org and GVC context at the start of a conversation
Be Specific
Provide specific values for resources (memory, CPU, replicas) when possible
Confirm Destructive Actions
Configure your AI assistant to confirm before executing delete or update operations
Use Tags
Add tags to resources for better organization and filtering
Recommended Prompts Structure
For the best results, structure your prompts with:- Context: Org and GVC
- Action: What you want to do
- Target: Which resource
- Details: Specific configuration values