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데이터분석 LangGraph Agent (w. MCP)

@gongwon-nayeon

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基本情報

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トランスポート

stdio

作者

gongwon-nayeon

데이터분석 LangGraph Agent について

DataAnalysis Agent using LangGraph & MCP server and client

#langgraph#data-analysis#mcp

데이터분석 LangGraph Agent の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "langgraph-mcp-dataanalysis": {
      "command": "python",
      "args": [
        "data_server.py"
      ]
    }
  }
}

데이터분석 LangGraph Agent のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

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 데이터분석 LangGraph Agent (w. Model Context Protocol)?

This project implements a Python-based MCP (Model Context Protocol) server and client for data analysis tasks (statistics, visualization, modeling) and integrates them with a LangGraph Agent using the langchain-mcp-adapters library. It is designed for users who want to build a data analysis agent that can understand natural language requests and perform operations on CSV datasets.

How to use 데이터분석 LangGraph Agent (w. Model Context Protocol)?

Install the required dependency langchain-mcp-adapters, then run python data_server.py and python data_client.py. Users can then send natural language requests such as “Give me the statistics of the petal length column in iris_data.csv” or “Visualize the distribution of the sepal length column in iris_data.csv”.

Key features of 데이터분석 LangGraph Agent (w. Model Context Protocol)

  • Python‑based MCP server and client setup
  • Data statistics, visualization, and modeling capabilities
  • Integration with LangGraph Agent via langchain-mcp-adapters
  • Natural language user input for data requests
  • Works with CSV datasets (e.g., iris_data.csv)

Use cases of 데이터분석 LangGraph Agent (w. Model Context Protocol)

  • Compute and return statistical summaries for a specific column of a CSV file
  • Generate a visualization of a column’s distribution
  • Train a predictive model using selected features and output the results
  • Perform ad‑hoc data analysis through conversational agent interaction

よくある質問

1

What analysis tasks does this agent support?

The agent supports computing statistics, generating visualizations, and training machine learning models on CSV data.

2

What are the required dependencies?

You need Python, the `langchain-mcp-adapters` package, the MCP Python SDK, and any dependencies listed in the project (implicitly `pandas`, `matplotlib`, `scikit-learn`, etc. for data tasks).

3

Where does the input data come from?

The user provides a CSV file – the README example uses `iris_data.csv`.

4

Is this project intended for production use?

The README states that this project is part of a Fastcampus course on building AI agents; it is an educational implementation.

5

How do I run the server and client?

Run `python data_server.py` and in another terminal run `python data_client.py`. User input is then accepted via the client.

コメント

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