Inspektor Gadget MCP Server bridges Inspektor Gadget의 모델 Context Protocol (MCP)을 통해 LLMs와 낮은 수준의 커널 관찰성. 그것은 원료 eBPF-powered telemetry-DNS 추적, TCP 연결, 프로세스 실행, 파일 활동, syscalls, 그리고 AI 에이전트가 이유를 할 수있는 행동 인텔리전스에, 활성화 ** 데이터 구동 루트 분석 ** 직접 IDE 또는 AI 채팅 인터페이스.

특징

• AI-powered root cause analysis — LLMs는 여러 개의 가제트를 통해 낮은 수준의 커널 데이터를 자신감을 가지며, 추측을 방지하고, 증거로 추측을 대체합니다. -Full gadget Lifecycle Management — Deploy, run, stop, and retrieve results from Inspektor Gadget tools 이제 AI 채팅을 떠나지 않고. -Foreground & background modes - 빠른 디버깅 또는 연속 관찰성을 위해 배경으로 전경에서 실행된 가제트, 그 결과가 준비될 때 검색합니다.
• **Dynamic 도구 등록 ** - 각 가제트는 자체 MCP 도구 (예를 들어, gadget_trace_dns], gadget_trace_tcp]), 매개 변수, 필드 설명 및 가제트 메타데이터에서 자동으로 생성됩니다.

Inspektor Gadget MCP Server

The Inspektor Gadget MCP Server bridges Inspektor Gadget’s low-level kernel observability with LLMs through the Model Context Protocol (MCP). It turns raw eBPF-powered telemetry—DNS traces, TCP connections, process executions, file activity, syscalls, and more—into actionable intelligence that AI agents can reason over, enabling data-driven root cause analysis directly from your IDE or AI chat interface.

flowchart LR
    User["👤 User(IDE / Chat)"]
    LLM["🤖 LLM"]
    MCP["⚙️ IG MCP Server"]
    IG["🔍 Inspektor Gadget"]
    Kernel["🐧 Linux Kernel(eBPF)"]
    K8s["☸️ KubernetesCluster"]

    User -- prompt --> LLM
    LLM -- MCP tool calls --> MCP
    MCP -- run gadgets --> IG
    IG -- eBPF hooks --> Kernel
    Kernel -. telemetry .-> IG
    IG -. enriched data .-> MCP
    MCP -. structured JSON .-> LLM
    LLM -- analysis & RCA --> User
    IG -- metadata --> K8s

Features

• AI-powered root cause analysis — LLMs correlate low-level kernel data across multiple gadgets to pinpoint issues with confidence, replacing guesswork with evidence.
• Full gadget lifecycle management — Deploy, run, stop, and retrieve results from Inspektor Gadget tools without ever leaving your AI chat.
• Foreground & background modes — Run gadgets in foreground for quick debugging or in background for continuous observability, then retrieve results when ready.
• Dynamic tool registration — Each gadget becomes its own MCP tool (e.g., gadget_trace_dns, gadget_trace_tcp), with parameters, field descriptions, and filtering automatically generated from gadget metadata.
• Read-only mode — Restrict the server to non-destructive operations for safe production use.
• Flexible deployment — Run as a local binary (stdio), Docker container, or deploy directly into your Kubernetes cluster (HTTP transport).

Background

The observability gap