mcp-server-wikipedia
Provides tools for searching Wikipedia articles, getting summaries, inspecting table of contents, and retrieving specific sections or full pages using a progressive retrieval strategy to minimize token usage.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-server-wikipediaSearch for light reactions and summarize top result"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-server-wikipedia 📚
This project exposes Wikipedia as an MCP server using a Progressive Retrieval Strategy. It is designed to minimize token usage by allowing LLMs to "scout" information before fetching large bodies of text.
The Problem: Token Waste
Wikipedia integrations often fetch multiple full pages up front, then decide what mattered. This fills the context window with irrelevant data and increases latency and cost.
Related MCP server: Local Search MCP Server
The Solution: The Librarian Philosophy
This server implements a "Progressive Retrieval Ladder." Like a librarian helping you find a specific book, it encourages the model to:
Search for several candidate titles.
Summarize the candidates to find the right one.
Inspect the TOC to find the relevant section.
Fetch only the specific section OR the full page only if necessary.
graph TD
A[Search Articles] --> B[Get Summaries]
B --> C{Correct Page?}
C -- No --> A
C -- Yes --> D[Get TOC]
D --> E[Get Section / Page]Tools
search_articles(query, limit=5): Top matching pages with snippets.get_summaries(titles): Compact summaries for multiple candidate pages.get_toc(title): Table of contents / section map for a page.get_section(title, section): Retrieve a single section by index or title.get_page(title): Retrieve the full plain-text page.
Token Efficiency Benchmark
In deterministic testing, this progressive strategy achieves up to 80% token reduction compared to naive full-page retrieval. Detailed results can be found in BENCHMARK.md.
Strategy | Token Usage (Avg) |
Naive (Full Page) | ~100% |
MCP (Progressive) | ~20% |
Quick Start
Installation
From PyPI:
pip install mcp-server-wikipediaOr run it directly via npx (if using the JS wrapper) or the python entry point:
python -m mcp_server_wikipediaFor development:
git clone https://github.com/surendranb/wikipedia-mcp-server.git
cd wikipedia-mcp-server
python3 -m venv .venv
source .venv/bin/source
pip install -e .Run
wikipedia-mcp-serverMCP Client Configuration
Claude Desktop
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"wikipedia": {
"command": "wikipedia-mcp-server"
}
}
}Cursor / VS Code
Specify the wikipedia-mcp-server command in your MCP settings.
Example Prompts
"Search for 'photosynthesis light dependent reactions' and summarize the top 3 candidates."
"What molecules are produced during the light-dependent reactions of photosynthesis? Search first, then fetch only the relevant section."
Development
Run tests:
python -m unittest discover -s tests -p "test_*.py" -vRun benchmarks:
pip install -e ".[benchmark]"
python scripts/benchmark_token_efficiency.pyContributing
We value simplicity and surgical efficiency. If you have an improvement that maintains the single-file architecture and enhances retrieval precision, we welcome your input. See CONTRIBUTING.md.
License
MIT License. See LICENSE for details.
Maintenance
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