MCP Data Analytics Server

@Edwin1719

代码仓库

基本信息

分类

数据与分析

许可证

MIT license

传输方式

stdio

作者

Edwin1719

关于 MCP Data Analytics Server

Un potente servidor de análisis de datos construido con FastMCP que proporciona herramientas especializadas para el procesamiento, análisis y visualización de datos, accesible a través de una interfaz web moderna construida con Streamlit.

MCP Data Analytics Server 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

MCP Data Analytics Server 工具

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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 🔧 MCP Data Analytics Server?

It is an MCP (Model Context Protocol) server built with FastMCP that provides tools for file processing, statistical data analysis, interactive visualization with Plotly, and web tasks like GitHub search and scraping. It exposes a Streamlit web interface for natural‑language interaction, targeting data analysts and developers who want to automate analysis workflows.

How to use 🔧 MCP Data Analytics Server?

Clone the repository, install Python dependencies via pip install -r requirements.txt, copy .env.example to .env and add your OpenAI API key. Run the MCP server with python server.py and the Streamlit client with streamlit run app.py, then open http://localhost:8501. Use natural language prompts to invoke one of the eleven built‑in tools.

Key features of 🔧 MCP Data Analytics Server

File management: analysis, creation, and reading of documents.
Data analysis: statistics, pivot tables, and type detection.
Interactive visualizations using Plotly (bar, line, etc.).
Web tools: GitHub repository search and web scraping.
Format conversion between CSV, JSON, Excel, and Parquet.

Use cases of 🔧 MCP Data Analytics Server

Analyze a sales CSV and view summary statistics.
Create a bar chart of monthly sales data.
Search GitHub for Python data‑analysis repositories.
Convert an Excel file to JSON format for further processing.

常见问题

What are the system requirements?

Python 3.8 or higher and an OpenAI API key. All other dependencies are listed in `requirements.txt`.

How do I install and run the server?

Clone the repo, install requirements, configure the `.env` file with your OpenAI API key, then run `python server.py` and `streamlit run app.py` in separate terminals.

What tools are included?

Eleven tools: file analysis/creation, PDF/TXT/CSV reading, statistical analysis, advanced pivot tables, Plotly visualization, GitHub repo search, CSS‑based web scraping, URL file download, and data‑format conversion.

Does the server require an OpenAI API key?

Yes. The `.env` file must contain a valid `OPENAI_API_KEY` for the NLP‑driven analysis tools to work.

What license is the project under?

MIT License – see the LICENSE file in the repository for full terms.

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