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.
@codingaslu
基本信息
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.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"Streamlit-as-an-MCP-Host": {
"command": "uv",
"args": [
"pip",
"install",
"-r",
"requirements.txt"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
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。
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.
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.
summarize_wikipedia_article tooldeepseek-r1:1.5b)Python 3.8+, Ollama installed and running locally with the `deepseek-r1:1.5b` model, and an internet connection to fetch Wikipedia articles.
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`.
Run `uv run -- updated_client.py http://localhost:8000/sse https://en.wikipedia.org/wiki/Python_(programming_language)` (replace the URLs as needed).
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".
It uses the `deepseek-r1:1.5b` model by default, but you can change it in `ollama_server.py`.

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