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TitanicAIAnalysis: Análisis de Datos con Claude y MCP Server

@IzarLabs

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

IzarLabs

关于 TitanicAIAnalysis: Análisis de Datos con Claude y MCP Server

MCP Server para análisis de datos sobre dataset del Titanic

#python#analysis#titanic-kaggle#claude-desktop#mcp-server

TitanicAIAnalysis: Análisis de Datos con Claude y MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "TitanicAIAnalysis": {
      "command": "uv",
      "args": [
        "init",
        "."
      ]
    }
  }
}

TitanicAIAnalysis: Análisis de Datos con Claude y MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is 🚢 TitanicAIAnalysis: Análisis de Datos con Claude y MCP?

It is an educational MCP server that exposes the famous Titanic dataset (from Kaggle) as structured resources and tools for analysis with Claude. Designed for developers and data analysts who want to learn how to integrate structured data with large language models using the Model Context Protocol.

How to use 🚢 TitanicAIAnalysis: Análisis de Datos con Claude y MCP?

Install Python 3.10+, clone the repository, create a virtual environment with uv, install dependencies (mcp[cli] and pandas), and ensure Titanic.csv is present. Run the server directly with python main.py, or configure it in Claude Desktop using the provided start scripts (inicio.sh/inicio.bat) or via mcp install main.py. Once connected, Claude can access resources and tools via the MCP interface.

Key features of 🚢 TitanicAIAnalysis: Análisis de Datos con Claude y MCP

  • Two resources: full passenger dataset and pre-calculated survival statistics.
  • One tool to search for passengers by name.
  • Dynamic data loading on each request.
  • Structured data exposure for LLM analysis.
  • Easy integration with Claude Desktop.
  • Educational example of MCP server implementation.

Use cases of 🚢 TitanicAIAnalysis: Análisis de Datos con Claude y MCP

  • Ask Claude for overall survival statistics from the Titanic.
  • Compare survival rates between passenger classes.
  • Search for specific passengers (e.g., surname "Astor").
  • Analyze correlations like gender, class, and fare vs survival.
  • Generate simple textual analysis without manually uploading files.

常见问题

1

What makes this different from just sending a CSV file to Claude?

The server exposes data as structured MCP resources, allowing Claude to access pre-processed statistics and use a search tool dynamically—without manual file uploads. Data is always fresh from the server.

2

What are the runtime requirements?

Python 3.10 or higher, the `mcp[cli]` and `pandas` packages, and Claude Desktop (for integration). The Titanic.csv dataset is included in the repository.

3

Where does the data come from?

The dataset is the classic Titanic passenger list from Kaggle, provided as `Titanic.csv` in the repository. No external API is used.

4

Are there any limitations?

Large datasets may exceed Claude’s context window. Resources are read on each request (no caching). Advanced visualizations are not built-in; you would need to add custom tools.

5

What transport or authentication does the server use?

It uses the standard MCP protocol over stdio. No authentication is implemented—designed for local, trusted environments.

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