데이터분석 LangGraph Agent (w. Model Context Protocol)

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

FAQ from 데이터분석 LangGraph Agent (w. Model Context Protocol)

What analysis tasks does this agent support?

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

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).

Where does the input data come from?

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

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.

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.