nabchan-mcp-server

Name: nabchan-mcp-server
Author: backpaper0

@backpaper0

代码仓库

关于 nabchan-mcp-server

暂无概览

基本信息

分类

其他

许可证

MIT

运行时

python

传输方式

stdio

发布者

backpaper0

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "nabchan-mcp-server": {
      "command": "uv",
      "args": [
        "run",
        "-m",
        "tools.build_index"
      ]
    }
  }
}

工具

未检测到工具

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。

概览

What is nabchan-mcp-server?

nabchan-mcp-server is an experimental MCP server that returns information about the Nablarch framework based on its official documentation. It uses DuckDB and Lindera for full-text search of the documentation content.

How to use nabchan-mcp-server?

You can run it using Docker (with stdio transport) or via ToolHive. The README provides VSCode configuration examples for both stdio and SSE transports. For development, set up Python 3.11, uv, Git, and Docker.

Key features of nabchan-mcp-server

read_document – Converts a Nablarch documentation URL to markdown.
search_document – Searches the documentation and returns title, URL, and summary.
Index built from Nablarch HTML docs using morphological analysis.
Runs locally with Python and DuckDB.
Supports stdio and SSE transport types.

Use cases of nabchan-mcp-server

Ask questions about Nablarch in GitHub Copilot Chat (Agent mode).
Search for specific Nablarch topics or components.
Retrieve markdown versions of official Nablarch documentation pages.
Integrate Nablarch documentation queries into AI‐assisted development tools.

FAQ from nabchan-mcp-server

Is the server production‑ready?

No, this is an experimental project with room for improvement.

What are the system requirements for development?

Python 3.11, uv, Git, and Docker are required. The index must be built using uv run -m tools.build_index.

Where does the documentation data come from?

Data is extracted from the official Nablarch documentation HTML files. Text is extracted with BeautifulSoup, summarized (via LLM), and indexed in DuckDB with Lindera for full‑text search.