MCP-name： io.github.xorrkaz/cml-mcp

概述

cml-mcp 将人工智能助手的力量带入您的网络实验室！该工具允许您通过 Claude Desktop、Claude Code 和 Cursor 等 AI 应用，使用自然语言与思科建模实验室（CML）交互。

与其点击菜单或写脚本，只需用简单的英语告诉AI你想做什么——比如“创建一个带两台路由器的新实验室并配置OSPF”或“显示Router1上的运行配置”——然后观察它自动完成。

这通过模型上下文协议（MCP）实现，这是人工智能应用与外部工具和服务交互的标准方式。可以把它看作是给你的人工智能助手直接连接到你的CML服务器。

特色

• 创建实验室拓扑： 创建新实验室并定义网络拓扑的工具，可从零开始或使用完整拓扑定义。
• 查询状态： 获取实验室、节点、链接、注释及CML服务器本身详细信息的工具。
• 控制实验室和节点： 用于启动、停止和清除实验室或单个节点的工具。

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.).