Tuner MCP Server Integration (CloudKeeper Tuner)

The Tuner MCP (Model Context Protocol) Server brings CloudKeeper Tuner directly into AI-powered development environments, allowing users to interact with cloud optimization data through natural language. Instead of navigating dashboards, users can simply ask questions such as "Show me my top recommendations" or "How much can I save?" and receive instant answers within their preferred AI assistant.

Built on the open Model Context Protocol (MCP) standard, the integration works with popular AI tools including Cursor, Claude Desktop, Kiro, and VS Code Copilot, enabling teams to access Tuner insights without interrupting their existing workflows. Whether you're troubleshooting infrastructure, reviewing cloud spend, or optimizing resources, Tuner data is available where you already work.

Security remains unchanged. The MCP Server leverages the same authentication, role-based access control (RBAC), and tenant isolation already enforced by CloudKeeper, ensuring users only access data they are authorized to view. No additional permissions or security configurations are required.

The initial release provides read-only access to key Tuner capabilities, including cloud cost optimization recommendations with natural-language filtering, potential and realized savings analytics, account synchronization health, data freshness, and connected account inventory. This enables engineers, FinOps teams, and cloud administrators to quickly retrieve operational insights without switching between applications.

The Tuner MCP Server is now available across all Tuner-enabled production environments, including AWS and GCP deployments. Setup is straightforward: users simply configure their tenant-specific MCP endpoint in their AI tool, authenticate using their existing CloudKeeper credentials, and immediately begin querying their cloud optimization data through natural language. This integration makes cloud cost optimization faster, more accessible, and seamlessly embedded into everyday engineering workflows.