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MCP Documentation Server

@andrea9293

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

andrea9293

提交者

Andrea Bravaccino

关于 MCP Documentation Server

MCP Documentation Server - Bridge the AI Knowledge Gap.

#documentation#rag#knowledge

MCP Documentation Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "documentation": {
      "command": "npx",
      "args": [
        "-y",
        "@andrea9293/mcp-documentation-server"
      ]
    }
  }
}

MCP Documentation Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is MCP Documentation Server?

A TypeScript-based Model Context Protocol (MCP) server that provides local-first document management and semantic search. Documents are stored in an embedded Orama vector database with hybrid search (full‑text + vector), intelligent chunking, and local AI embeddings — no external database or cloud service required.

How to use MCP Documentation Server?

Configure an MCP client (e.g., Claude Desktop) to run npx -y @andrea9293/mcp-documentation-server. The web UI starts automatically on port 3080. Use tools like add_document, search_all_documents, and process_uploads to manage and search documents. All environment variables are optional.

Key features of MCP Documentation Server

  • Local-first document management and semantic search
  • Hybrid full-text and vector search via Orama
  • AI-powered search with optional Gemini API key
  • Built-in web dashboard with drag-drop file uploads
  • Parent‑child chunking for context‑preserving retrieval
  • LRU embedding cache and streaming file reader

Use cases of MCP Documentation Server

  • Indexing project documentation for instant semantic lookup
  • Searching across code comments, markdown files, and PDFs
  • Providing LLMs with richer context via neighboring chunk retrieval
  • Managing a personal knowledge base without cloud dependencies
  • Uploading and processing .txt, .md, or .pdf files in bulk

常见问题

1

Where is data stored?

All data resides in `~/.mcp-documentation-server/` (or a custom path via `MCP_BASE_DIR`). No external database or cloud service is used.

2

Is an external database or cloud service required?

No. The embedded Orama vector database runs locally; all embeddings are computed on‑device with Transformers.js.

3

Do I need a Gemini API key?

No. Without `GEMINI_API_KEY`, only local embedding‑based search tools are available. The AI‑powered search (`search_documents_with_ai`) requires the key.

4

Can I change the embedding model after adding documents?

Yes, but changing `MCP_EMBEDDING_MODEL` requires re‑adding all documents because embeddings from different models are incompatible. The Orama database is recreated automatically when the vector dimension changes.

5

How do I access the web UI?

The web UI starts automatically on port 3080 when the MCP server launches. Open `http://localhost:3080`. To run it standalone (without the MCP server), use `npm run web` or `npm run web:build`.

评论

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