mcp 이름: io.github.xorrkaz/cml-mcp

개요

cml-mcp AI Assistants의 힘을 네트워크 실험실에 가져옵니다! 이 도구는 Claude Desktop, Claude Code 및 Cursor와 같은 AI 응용 프로그램을 통해 자연 언어를 사용하여 Cisco Modeling Labs (CML)과 상호 작용할 수 있습니다.

메뉴를 클릭하거나 스크립트를 작성하는 대신, 단순히 "두 개의 라우터와 새로운 실험실을 제어하고 OSPF를 구성"또는 " Router1에서 실행 설정 구성을 참조하십시오"와 같이 일반 영어에서 할 수있는 AI를 알려줍니다.

이 모델 Context Protocol (MCP)을 통해 수행되며, AI 애플리케이션의 표준 방식은 외부 도구 및 서비스와 상호 작용합니다. AI Assistant가 CML 서버에 직접 연결되도록 생각하십시오.

특징

• ** 실험실 심리학 : ** 새로운 실험실을 만들고 스크래치에서 네트워크 토폴로지를 정의하거나 전체 토폴로지 정의합니다.
• ** 쿼리 상태:** 실험실, 노드, 링크, 주석 및 CML 서버 자체에 대한 상세 상태 정보를 검색하는 도구.
• Control Labs 및 노드: 도구가 시작, 중지, 및 와이퍼 또는 필요에 따라 개별 노드.

Model Context Protocol (MCP) Server for Cisco Modeling Labs (CML)

mcp-name: io.github.xorrkaz/cml-mcp

Overview

cml-mcp brings the power of AI assistants to your network lab! This tool allows you to interact with Cisco Modeling Labs (CML) using natural language through AI applications like Claude Desktop, Claude Code, and Cursor.

Instead of clicking through menus or writing scripts, simply tell the AI what you want to do in plain English—like “Create a new lab with two routers and configure OSPF” or “Show me the running config on Router1”—and watch it happen automatically.

This is accomplished through the Model Context Protocol (MCP), a standard way for AI applications to interact with external tools and services. Think of it as giving your AI assistant a direct connection to your CML server.

Features

• Create Lab Topologies: Tools to create new labs and define network topologies from scratch or using full topology definitions.
• Query Status: Tools to retrieve detailed status information for labs, nodes, links, annotations, and the CML server itself.
• Control Labs and Nodes: Tools to start, stop, and wipe labs or individual nodes as needed.
• Manage CML Users and Groups: Tools to list, create, and delete local users and groups (requires admin privileges).
• Visual Annotations: Add visual elements (text, rectangles, ellipses, lines) to lab topologies for documentation and organization.
• Link Management: Connect nodes, configure link conditioning (bandwidth, latency, jitter, loss), and control link states.
• Packet Capture: Start, stop, and retrieve packet captures (PCAP) from network links for traffic analysis with Wireshark or other tools.
• Node Configuration: Configure node startup configurations and send CLI commands to running devices.
• Run Commands on Devices: Using PyATS, MCP clients can execute commands on virtual devices within CML labs.
• Console Log Access: Retrieve console logs from running nodes for troubleshooting and monitoring, with support for selecting specific serial console ports.
• Modular Architecture: Tools are organized into logical modules (labs, nodes, links, pcap, etc.) for maintainability and extensibility.
• Access Control Lists (HTTP Mode): When running in HTTP transport mode, you can restrict which users can access which tools using a YAML-based ACL configuration file.
• Observability: Optional OpenTelemetry tracing exports spans for every tool call to any OTLP-compatible backend (Jaeger, Grafana Tempo, Datadog, etc.).