Akyn Ai MCP Server

@IlyesTal

代码仓库主页

基本信息

分类

开发工具

许可证

MIT

传输方式

stdio

作者

IlyesTal

提交者

Ilyes TALBI

关于 Akyn Ai MCP Server

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

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

Akyn Ai MCP Server 工具

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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 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

常见问题

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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