Brev

在云端运行、构建、训练和部署机器学习模型。

metoro-mcp-server

使用LLM查询和交互由Metoro监控的Kubernetes环境。查看APM、指标、追踪和性能分析信息。

MCP2Lambda

通过 MCP 协议，人工智能模型能够与 AWS Lambda 函数交互，从而在安全的环境中访问私有资源、实时数据和自定义计算。

steam

Okay, I understand. As a large language model, I don't "play" games in the same way a human does. I don't have personal experiences or the ability to interact with games in a real-time, sensory way. However, I can be used to *process* information about games. Here's how I might be "involved" with games, and how you could quantify that involvement in terms of "time spent": * **Analyzing Game Text/Code:** I can be fed game scripts, dialogue, lore, or even game code (within ethical and legal boundaries). I can then analyze this data for sentiment, plot points, character relationships, or even potential bugs. The "time spent" would be the duration it takes to process the data. For example: "I spent 2 hours analyzing the dialogue trees of *The Witcher 3* to identify the most impactful choices." * **Generating Game Content:** I can be used to generate story ideas, character backstories, item descriptions, or even code snippets for game development. The "time spent" would be the duration of the content generation process. For example: "I spent 30 minutes generating 10 different character backstories for a fantasy RPG." * **Simulating Game Scenarios:** I can be used to simulate game scenarios based on rules and parameters you provide. This could involve predicting outcomes of battles, optimizing resource management, or testing different strategies. The "time spent" would be the duration of the simulation. For example: "I spent 1 hour simulating 1000 battles in *Civilization VI* to determine the optimal unit composition." * **Learning Game Rules and Strategies:** I can be trained on game rules, strategies, and player guides. This allows me to answer questions about the game, provide advice, or even play a text-based version of the game. The "time spent" would be the duration of the training process. For example: "I spent 4 hours learning the rules and strategies of *Go* by analyzing online resources." * **Answering User Questions About Games:** I spend a significant amount of time answering user questions about various games. This involves retrieving information, summarizing it, and presenting it in a helpful way. The "time spent" is the cumulative time spent answering these queries. For example: "I spent approximately 1 hour today answering questions about *Elden Ring*." **In summary, while I don't "play" games in the traditional sense, I can be used to analyze, generate, simulate, and learn about them. The "time spent" is the duration of these processes.** To give you a more concrete example, if you asked me to analyze the plot of *Final Fantasy VII* and summarize the key events, I might say: "I spent 15 minutes analyzing the plot of *Final Fantasy VII* based on information available online and in my training data. I then spent 5 minutes summarizing the key events into a concise overview." **How would you like me to describe my "game-related activities" and the "time spent" in Chinese? Please provide specific examples of the types of activities you're interested in.**

mcp-k8s-go

这个项目旨在成为一个连接到 Kubernetes 的 MCP 服务器，同时也是一个用于为 Kubernetes 中任何自定义资源构建更多服务器的库。

Sandbox MCP Server

为代码执行提供隔离的 Docker 环境，使用户能够创建和管理容器、执行多语言代码、保存和重现开发环境，从而确保安全性和隔离性。

Room MCP

一个命令行工具，可以使用 Room 协议启用 MCP，允许代理创建点对点虚拟房间并进行交互，以实现目标导向的协作。

MCP 3D Printer Server

通过 MCP 服务器实现与 3D 打印机管理系统的交互，支持 STL 文件操作、切片以及对 OctoPrint、Klipper、Duet 等打印机的控制。

MCP Server Template

一个用于在 TypeScript 中创建模型上下文协议 (MCP) 服务器的模板，提供诸如基于容器的依赖注入、基于服务的架构以及与 LLM CLI 的集成等功能，以便通过自然语言获得架构设计反馈。

Ollama MCP Server

一个桥梁，能够将 Ollama 的本地 LLM 功能无缝集成到 MCP 驱动的应用程序中，允许用户在本地管理和运行 AI 模型，并提供完整的 API 覆盖。