広告掲載
MCP.so
ログイン

Wikipedia Summarizer MCP Server

@codingaslu

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

作者

codingaslu

Wikipedia Summarizer MCP Server について

An MCP (Model Context Protocol) server that fetches and summarizes Wikipedia articles using Ollama LLMs, accessible via both command-line and Streamlit interfaces. Perfect for quickly extracting key information from Wikipedia without reading entire articles.

#wikipedia#summarizer#mcp-server

Wikipedia Summarizer MCP Server 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": {
    "Streamlit-as-an-MCP-Host": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-r",
        "requirements.txt"
      ]
    }
  }
}

Wikipedia Summarizer 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 Wikipedia Summarizer MCP Server?

It is an MCP server that fetches Wikipedia articles and summarizes them using local Ollama LLM models. It includes a command-line client and a Streamlit web interface for easy interaction.

How to use Wikipedia Summarizer MCP Server?

Install Python 3.8+ and Ollama (with the deepseek-r1:1.5b model), clone the repository, and install dependencies with uv pip install -r requirements.txt. Start the server with uv run -- ollama_server.py (available at http://localhost:8000/sse). Use the command-line client with uv run -- updated_client.py <server_url> <wikipedia_url> or launch the Streamlit interface with uv run -- streamlit run streamlit_new.py, then enter the server URL and article URL in the browser.

Key features of Wikipedia Summarizer MCP Server

  • MCP server providing a summarize_wikipedia_article tool
  • Command-line client for direct summarization requests
  • Streamlit web interface for interactive use
  • Uses Ollama LLM models (default deepseek-r1:1.5b)
  • Fetches Wikipedia article content and converts to markdown

Use cases of Wikipedia Summarizer MCP Server

  • Summarize any Wikipedia article using a local LLM
  • Automate article summarization via command-line client
  • Provide a web UI for non-technical users to get summaries

よくある質問

1

What are the prerequisites for running the server?

Python 3.8+, Ollama installed and running locally with the `deepseek-r1:1.5b` model, and an internet connection to fetch Wikipedia articles.

2

How do I install and run the server?

Clone the repository, run `uv pip install -r requirements.txt`, then start the server with `uv run -- ollama_server.py`. The server listens at `http://localhost:8000/sse`.

3

How do I use the command-line client?

Run `uv run -- updated_client.py http://localhost:8000/sse https://en.wikipedia.org/wiki/Python_(programming_language)` (replace the URLs as needed).

4

How do I use the Streamlit interface?

Run `uv run -- streamlit run streamlit_new.py`, open the provided URL in a browser, enter the MCP server URL and a Wikipedia article URL, then click "Fetch and Summarize Article".

5

What Ollama model does the server use by default?

It uses the `deepseek-r1:1.5b` model by default, but you can change it in `ollama_server.py`.

コメント

「その他」の他のコンテンツ

Figs, Relationship Expert

Figs O'Sullivan, LMFT

Couples therapist Figs O'Sullivan (LMFT, 17 years, endorsed by Sue Johnson, creator of EFT) inside your AI. Ask relationship questions answered from his clinical writing, name the pattern in a fight, get a three-step rep

Lemon.io

lemon-io

Request dedicated senior developers to work on your project, write job descriptions, and prep technical interviews — all without leaving your Claude chat. Just describe what you need, and Lemon.io MCP does the rest. 1–3

FastMCP v2 🚀

jlowin

🚀 The fast, Pythonic way to build MCP servers and clients.

26.0K

Screenpipe

screenpipe

YC (S26) | Open Computer History | Continuously record your company computer work, map your workflows, help you find work worth automating, and power your agents' context

22.0K

Awesome Mlops

visenger

A curated list of references for MLOps

14.0K
広告掲載