Mcp Documentation Server

Name: Mcp Documentation Server
Author: andrea9293

@andrea9293

代码仓库

关于 Mcp Documentation Server

MCP Documentation Server - Bridge the AI Knowledge Gap.

#documentation #rag #knowledge

基本信息

分类

记忆与知识

传输方式

stdio

发布者

andrea9293

提交者

Andrea Bravaccino

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"
      ]
    }
  }
}

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 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

FAQ from MCP Documentation Server

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.

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.

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.

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.

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.

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