Akyn Ai MCP Server

@IlyesTal

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基本情報

カテゴリ

開発者ツール

ライセンス

MIT

トランスポート

stdio

作者

IlyesTal

投稿者

Ilyes TALBI

Akyn Ai MCP Server について

Turn any data source into an MCP server in 5 minutes. Build AI-agents-ready knowledge bases.

Akyn Ai MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "akyn-sdk": {
      "command": "docker",
      "args": [
        "run",
        "-p",
        "6333:6333",
        "qdrant/qdrant"
      ]
    }
  }
}

Akyn Ai MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Akyn Ai?

Akyn Ai is an SDK that lets you create MCP (Model Context Protocol) servers from any data source. It turns documents, PDFs, websites, or raw text into a queryable knowledge base that AI assistants like Claude and Cursor can access directly — no infrastructure needed.

How to use Akyn Ai?

Install with npm install akyn-ai, then use the KnowledgeBase class to add content and serve it via stdio (for Cursor/Claude Desktop) or HTTP. You can also use the CLI with npx akyn-ai --dir ./docs --name "My Docs". Configure your MCP client as shown in the README.

Key features of Akyn Ai

Multi-source ingestion: files, directories, URLs, raw text
Smart text chunking with configurable size and overlap
Flexible embeddings (OpenAI default; custom providers supported)
In‑memory and Qdrant vector stores (extensible interface)
Multiple transport options: stdio and HTTP
CLI for quick indexing without writing code

Use cases of Akyn Ai

Make project documentation searchable by Cursor or Claude Desktop
Build Retrieval-Augmented Generation (RAG) pipelines
Create custom AI assistants with domain‑specific knowledge
Index research papers, guides, or any text content

よくある質問

What is the difference between Akyn Ai and building an MCP server manually?

Akyn Ai provides a high‑level SDK that handles ingestion, chunking, embeddings, vector storage, and MCP transport automatically, reducing setup to a few lines of code.

What are the runtime requirements for Akyn Ai?

Node.js 18+ and an OpenAI API key (or a custom embeddings provider). The SDK is open‑source under the MIT license.

Where does the data (embeddings and documents) live?

By default, data is stored in‑memory (optionally persisted to disk). For production, you can use Qdrant (local Docker or Qdrant Cloud). You can also implement custom vector stores.

How do I connect Akyn Ai to Cursor or Claude Desktop?

Add an MCP server entry to `.cursor/mcp.json` or `claude_desktop_config.json` with the command `npx ts-node ./my-kb.ts` and your OpenAI API key.

How can I use Akyn Ai with custom embeddings?

Implement the `EmbeddingsProvider` interface (with `embed` and `embedBatch` methods) and pass it to the `KnowledgeBase` constructor. The default uses `OpenAIEmbeddings`.

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