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LangChain MCP Server

@TransData

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

TransData

LangChain MCP Server について

概要はまだありません

LangChain MCP Server の設定

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

LangChain MCP Server 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 LangChain MCP?

LangChain MCP is a repository that explores the Model Context Protocol (MCP) using the LangChain framework. It includes two core server components: a Twitter Server that fetches and returns relevant tweets based on a user query, and an ArXiv Server that retrieves relevant research papers from ArXiv, summarizing content, authors, and more.

How to use LangChain MCP?

Install the required dependencies using pip install -r requirements.txt. Then start the MCP server by running python server_name.py (replace server_name.py with the appropriate file for the desired component). The server will launch on its assigned port and be ready to handle requests.

Key features of LangChain MCP

  • Fetches relevant tweets based on a user query.
  • Retrieves relevant research papers from ArXiv.
  • Summarizes paper content and author information.
  • Implements the Model Context Protocol (MCP).
  • Built on the LangChain framework.

Use cases of LangChain MCP

  • Querying and summarizing current tweets about a specific topic.
  • Retrieving and summarizing recent research papers from ArXiv.
  • Exploring how MCP can be integrated with LangChain for data retrieval.

よくある質問

1

What dependencies are required?

Install dependencies via `pip install -r requirements.txt` before running any server.

2

How do I run the Twitter or ArXiv server?

Run `python server_name.py` where server_name.py is the script for the desired component (e.g., the Twitter server or ArXiv server).

3

What does MCP stand for?

MCP stands for Model Context Protocol, a protocol explored in this repository for connecting language models with external data sources.

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