OI-Wiki增强工具
通过利用OI-Wiki内容的向量搜索，增强大型语言模型的竞争编程知识，使模型能够检索相关的算法和技术。
MCP 服务配置
复制以下 JSON 到 OPClaw 或其他 MCP 客户端的配置文件中即可使用
{
"mcpServers": {
"oi-wiki": {
"args": [
"--directory",
"\u003cpath of MCP servers\u003e/mcp-oi-wiki",
"run",
"python",
"main.py"
],
"command": "uv"
}
}
}可用工具 （1 个）
该服务在 MCP 协议中暴露的工具，AI 可按需调用
search 1 个参数 需填 1 项
nOI Wiki 致力于成为一个免费开放且持续更新的编程竞赛知识整合站点,大家可以在这里获取与竞赛相关的、有趣又实用的知识。本工具能够在 OI-wiki 中搜索相关的知识点。nnquery 应该比较详细,与要实现的算法/目标相关。比如：“求一个图的最小生成树”,“维护区间加/区间求和”nn@ param query 描述要实现的需求n
必填参数：query
服务介绍
mcp-oi-wiki
Empower large models with the OI-Wiki boost!
How does it work?
We use Deepseek-V3 to generate summaries of the current 462 pages on OI-wiki, embed these summaries as semantic vectors, and establish a vector database.
During queries, the closest vector in the database is found, and the corresponding wiki markdown is returned.
Usage
Ensure you have uv.
First, download this repository:
cd
git clone --recurse-submodules https://github.com/ShwStone/mcp-oi-wiki.git
Then, open your MCP configuration file (mcpo or claude):
json
{
"mcpServers": {
"oi-wiki": {
"command": "uv",
"args": [
"--directory",
"/mcp-oi-wiki",
"run",
"python",
"main.py"
]
}
}
}
Update
You can generate your own db/oi-wiki.db.
Place your Silicon flow API key in the api.key file.
Then run:
sh
uv run script/request.py
Download the summary results to result.jsonl from the batch inference page.
Finally, run:
sh
uv run script/gendb.py
This will generate a new db/oi-wiki.db.
Thanks
- milvus-io/milvus-lite: A lightweight version of Milvus for the vector database
- OI-wiki/OI-wiki: :star2: Wiki of OI / ICPC for everyone. (A comprehensive online guide for a popular game, featuring cool arithmetic magic) for OI-wiki
- qdrant/fastembed: Fast, Accurate, Lightweight Python library to make State of the Art Embedding for CPU-based vector embedding
