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GenAI News Assistant MCP Server

@uriyahav

代码仓库

关于 GenAI News Assistant MCP Server

暂无概览

基本信息

分类

AI 与智能体

运行时

python

传输方式

stdio

发布者

uriyahav

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "MCP_SSE_Server": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "news-assistant-python",
        "."
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is GenAI News Assistant MCP Server?

It is a Model Context Protocol (MCP) server template that provides tools for fetching and analyzing news information using NewsAPI.org and a Large Language Model. It is intended for developers building AI assistants that need to search and understand recent news.

How to use GenAI News Assistant MCP Server?

Set up by cloning the repository, obtaining API keys for NewsAPI.org and an LLM, then build and run the Docker container for either the Python or TypeScript implementation. The server exposes MCP tools for news search and analysis and can be tested locally using the MCP Inspector.

Key features of GenAI News Assistant MCP Server

  • Fetches news via NewsAPI.org
  • Analyzes articles using an LLM
  • Extracts structured information and sentiment
  • Implements three MCP tools: search, extract info, extract key info/sentiment
  • Available in both Python and TypeScript
  • Runs inside Docker for easy deployment

Use cases of GenAI News Assistant MCP Server

  • Monitor news articles for specific topics or queries
  • Extract key entities and sentiment from news articles
  • Automate news summarization and analysis in AI workflows

FAQ from GenAI News Assistant MCP Server

What API keys are required?

You need a NewsAPI.org API key and an LLM API key, set as environment variables NEWS_API_KEY and LLM_API_KEY.

How do I run the server?

Build the Docker image for your chosen implementation (Python or TypeScript) and run the container, passing the API keys as environment variables.

What MCP tools does it provide?

Three tools: search_news (search for recent articles), extract_information_from_article (extract structured info from an article), and extract_key_info_and_sentiment (analyze key entities and sentiment).

What are the runtime requirements?

Docker is required; no direct dependency on Python or Node.js if using Docker. The server listens on port 3000 by default.

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