steam

steam
官方

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.**

游戏与游戏化
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README

MCP Steam 服务器

一个模型上下文协议 (MCP) 服务器,为 AI 助手提供 Steam 游戏上下文。此服务与 Steam API 集成,以获取用户游戏信息,并通过 MCP 协议公开这些信息,从而允许 AI 助手访问和理解用户的游戏活动和偏好。

安装

使用 Docker (推荐)

运行 MCP Steam 服务器最简单的方法是使用 Docker:

docker run --rm -i ghcr.io/dsp/mcp-server-steam:latest

配置

可以使用环境变量配置服务器:

# 必需配置
STEAM_API_KEY=你的_steam_api_key

开发

前提条件

  • OpenJDK 21
  • Docker (用于容器构建)
  • Git
  • devenv.sh

设置开发环境

  1. 克隆存储库:

    git clone https://github.com/dsp/mcp-steam.git
    cd mcp-steam
    
  2. 使用开发 shell:

    devshell shell
    

    这将设置所需的开发环境,并包含所有必要的依赖项。

  3. 构建项目:

    mvn package
    

在本地构建 Docker 镜像

docker build -t mcp-server-steam .

API 文档

该服务器实现了模型上下文协议 (MCP) 规范。 有关详细的 API 文档,请参阅 MCP 文档

贡献

欢迎贡献! 请随时提交 Pull Request。

许可证

MIT 许可证