Wikipedia Summarizer MCP Server

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

FAQ from Wikipedia Summarizer MCP Server

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.

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.

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).

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".

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.