Langgraph Mcp Dataanalysis
What is Langgraph Mcp Dataanalysis
langgraph-mcp-dataanalysis is a data analysis agent that utilizes LangGraph and MCP server and client to perform data statistics, visualization, and modeling.
Use cases
Use cases include generating statistics for specific data columns, visualizing data distributions, and training predictive models based on input features from datasets.
How to use
To use langgraph-mcp-dataanalysis, install the necessary package with ‘pip install langchain-mcp-adapters’, then run the server using ‘python data_server.py’ and the client with ‘python data_client.py’.
Key features
Key features include the ability to perform statistical analysis on data columns, visualize data distributions, and train models to predict outcomes based on selected features.
Where to use
langgraph-mcp-dataanalysis can be used in fields such as data science, machine learning, and statistical analysis, particularly for projects that require data processing and visualization.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Langgraph Mcp Dataanalysis
langgraph-mcp-dataanalysis is a data analysis agent that utilizes LangGraph and MCP server and client to perform data statistics, visualization, and modeling.
Use cases
Use cases include generating statistics for specific data columns, visualizing data distributions, and training predictive models based on input features from datasets.
How to use
To use langgraph-mcp-dataanalysis, install the necessary package with ‘pip install langchain-mcp-adapters’, then run the server using ‘python data_server.py’ and the client with ‘python data_client.py’.
Key features
Key features include the ability to perform statistical analysis on data columns, visualize data distributions, and train models to predict outcomes based on selected features.
Where to use
langgraph-mcp-dataanalysis can be used in fields such as data science, machine learning, and statistical analysis, particularly for projects that require data processing and visualization.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
데이터분석 LangGraph Agent (w. Model Context Protocol)
데이터 통계, 시각화, 모델링을 진행하는 Agent를 구현하기 위해 파이썬 기반의 MCP 서버 및 클라이언트를 구축하고, langchain-mcp-adapters 을 사용하여 Langgraph Agent와 연동하는 프로젝트
Installation & Getting Started
pip install langchain-mcp-adapters python data_server.py python data_client.py
🧑💻 User Input Examples
- iris_data.csv 파일의 petal length 컬럼의 통계를 내주세요.
- iris_data.csv 파일의 sepal length 컬럼의 분포를 시각화해주세요.
- iris_data.csv 데이터에서 sepal length,sepal width 를 피쳐로 사용해서 class_name 를 예측하는 모델을 학습하고 결과를 알려주세요.
Reference
✅ https://github.com/modelcontextprotocol/python-sdk
✅ https://github.com/langchain-ai/langchain-mcp-adapters
📍 본 프로젝트는 패스트캠퍼스 🚀 실전 AI Agent의 모든 것 : 34개 프로젝트로 MCP부터 GraphRAG Agent까지 (by.공원나연) 강의 중 LangGraph MCP Adapters - 데이터 분석 Agent🗒️ 에 해당하는 내용입니다.
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
