MCP OI-Wiki
Processes and returns OI-Wiki content in Markdown format, allowing AI to reference competitive programming documentation and algorithms directly
Utilizes Milvus Lite as a vector database to store and retrieve semantic vectors of OI-Wiki content, enabling semantic search capabilities for competitive programming knowledge
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 OI-Wikiexplain how to implement Dijkstra's algorithm for shortest path problems"
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-oi-wiki
让大模型拥有 OI-Wiki 的加成！
How does it work?
使用 Deepseek-V3 对 OI-wiki 当前的 462 个页面做摘要，将摘要嵌入为语义向量，建立向量数据库。
查询时，找到数据库中最接近的向量，返回对应的 wiki markdown。
Related MCP server: MCP Code Indexer
Usage
确保你拥有 uv。
首先，下载本仓库：
cd <path of MCP servers>
git clone --recurse-submodules https://github.com/ShwStone/mcp-oi-wiki.git然后打开你的 MCP 配置文件（mcpo 或 claude）：
{
"mcpServers": {
"oi-wiki": {
"command": "uv",
"args": [
"--directory",
"<path of MCP servers>/mcp-oi-wiki",
"run",
"python",
"main.py"
]
}
}
}Update
可以生成自己的 db/oi-wiki.db。
将 Silicon flow API key 放在 api.key 文件中。
然后运行：
uv run script/request.py在批量推理页面下载摘要结果到 result.jsonl。
最后运行：
uv run script/gendb.py生成新的 db/oi-wiki.db。
Thanks
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