CLIManaging CloudQueryMCP Server

What is MCP?

Model Context Protocol (MCP) is a standard for connecting AI assistants to data sources and tools. It provides a unified way for AI systems to access and interact with various data sources, including CloudQuery’s cloud asset inventory.

CloudQuery MCP Server

The CloudQuery MCP server exposes your cloud asset inventory through the MCP protocol, allowing AI assistants to:

  • Query your cloud resources using natural language
  • Get real-time information about your infrastructure
  • Perform asset discovery and analysis
  • Generate reports and insights

Setup

Prerequisites

  • CloudQuery CLI installed and configured
  • Active syncs with cloud providers
  • MCP-compatible AI assistant or tool

Installation

  1. Install the CloudQuery MCP server:
cloudquery plugin install cloudquery/mcp-server
  1. Configure the MCP server in your CloudQuery configuration:
kind: destination
spec:
  name: mcp-server
  path: cloudquery/mcp-server
  registry: cloudquery
  version: "latest"
  spec:
    # MCP server configuration
    port: 8080
    # Add your sync data sources
    sources: ["aws", "gcp", "azure"]
  1. Start the MCP server:
cloudquery sync --destination mcp-server

Usage

Once the MCP server is running, you can connect AI assistants to it using the MCP protocol. The server will expose your cloud asset inventory as queryable data.

Example Queries

  • “Show me all EC2 instances in production”
  • “What S3 buckets are publicly accessible?”
  • “List all databases across all cloud providers”
  • “Find resources with security vulnerabilities”

Configuration

The MCP server supports various configuration options:

  • Port: Specify the port for the MCP server
  • Sources: Define which sync sources to expose
  • Authentication: Configure access controls
  • Rate Limiting: Set query rate limits

Integration Examples

Claude Desktop

Add the CloudQuery MCP server to your Claude Desktop configuration:

{
  "mcpServers": {
    "cloudquery": {
      "command": "cloudquery",
      "args": ["mcp-server", "--port", "8080"]
    }
  }
}

Other AI Tools

The CloudQuery MCP server is compatible with any MCP client. Refer to your AI tool’s documentation for MCP integration instructions.

Security Considerations

  • Ensure proper authentication and authorization
  • Use HTTPS in production environments
  • Implement rate limiting to prevent abuse
  • Monitor access logs and usage patterns

Troubleshooting

Common Issues

  1. Connection Refused: Check that the MCP server is running and accessible
  2. Authentication Errors: Verify your CloudQuery credentials and permissions
  3. No Data Available: Ensure your syncs are running and have data

Debug Mode

Enable debug logging for troubleshooting:

cloudquery mcp-server --debug --port 8080

API Reference

The MCP server exposes the following capabilities:

  • query_assets: Query cloud assets with filters
  • get_asset_details: Get detailed information about specific assets
  • list_providers: List available cloud providers
  • get_schema: Get the schema for asset tables

For detailed API documentation, see the MCP specification.

Rate LimitingSecurity