Metoro MCP Server

Metoro’s MCP server bridges the gap between Kubernetes observability and conversational AI by exposing a rich set of telemetry APIs to LLMs through the Model Context Protocol. In practice, this means developers can ask an AI assistant questions like “What is the latency trend for service‑X?” or “Show me pods with high CPU usage” and receive instant, context‑aware answers that are grounded in real cluster data. The server translates standard MCP queries into calls against Metoro’s eBPF‑based telemetry backend, returning structured results that the AI can incorporate into its responses.

The core value of this server lies in its ability to democratize access to deep, code‑free instrumentation. Metoro’s platform automatically injects eBPF probes into running microservices, collecting fine‑grained metrics such as request latency, error rates, and resource consumption without requiring any application changes. By exposing these metrics via MCP, the server lets AI tools tap into this wealth of information without needing custom integrations or SDKs. For developers, this translates to faster troubleshooting, more informed decision‑making, and the ability to embed observability directly into IDEs or chat interfaces.

Key capabilities include:

• Queryable telemetry: Retrieve aggregated metrics, pod lists, and service health states through a unified MCP endpoint.
• Real‑time insights: Access live data streams from eBPF probes, enabling the AI to surface up‑to‑date cluster status.
• Secure authentication: Token‑based access tied to a Metoro account, ensuring that only authorized users can query sensitive telemetry.
• Zero‑code instrumentation: Leverage Metoro’s automatic eBPF injection, eliminating the need for custom exporters or sidecars.

Typical use cases span a wide range of development and operations scenarios. A DevOps engineer can ask the AI to “Show me any pods that have exceeded 80 % CPU in the last hour,” instantly receiving a filtered list and actionable recommendations. A product manager might request “What is the trend in error rates for service‑Y?” and get a concise chart embedded in the chat. During onboarding, new team members can query “What services are running on cluster‑A?” to gain a quick overview of the environment. In all cases, the MCP server provides a consistent, language‑agnostic interface that fits naturally into existing AI workflows.

Because it follows the open MCP specification, the Metoro server can be plugged into any LLM client that supports the protocol—Claude Desktop, OpenAI’s API, or custom tooling. This interoperability means teams can extend their conversational agents with deep Kubernetes telemetry without rewriting integration logic for each new platform. The result is a powerful, reusable bridge that turns raw observability data into actionable AI‑driven insights.