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🔧 MCP Data Analytics Server

@Edwin1719

リポジトリ

🔧 MCP Data Analytics Server について

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.

基本情報

カテゴリ

データと分析

ライセンス

MIT license

ランタイム

python

トランスポート

stdio

公開者

Edwin1719

設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.

FAQ from 🔧 MCP Data Analytics Server

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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