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Embedding MCP Server

@Geeksfino

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

ライセンス

MIT

トランスポート

stdio

作者

Geeksfino

Embedding MCP Server について

Build a knowledge base into a tar.gz and give it to this MCP server, and it is ready to serve.

Embedding 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": {
    "kb-mcp-server": {
      "command": "uv",
      "args": [
        "venv",
        "--python=3.10",
        "#",
        "or",
        "3.11,",
        "3.12,",
        "etc."
      ]
    }
  }
}

Embedding 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 Embedding MCP Server?

Embedding MCP Server is a Model Context Protocol (MCP) server implementation powered by txtai that provides semantic search, knowledge graph capabilities, and AI-driven text processing through a standardized interface. It includes a knowledge base builder tool for creating and managing knowledge bases from documents.

How to use Embedding MCP Server?

Install with uv pip install kb-mcp-server or pip install kb-mcp-server, then build a knowledge base using kb-build --input /path/to/documents --config config.yml and start the server with kb-mcp-server --embeddings /path/to/knowledge_base. Alternatively, use uvx --from kb-mcp-server@0.3.0 kb-mcp-server --embeddings /path/to/knowledge_base for no-install execution. The server can be configured via command-line arguments (e.g., --host, --port, --transport) or environment variables.

Key features of Embedding MCP Server

  • Unified vector database combining vector indexes, graph networks, and relational databases.
  • Semantic search based on meaning, not keywords.
  • Knowledge graph integration for automatic building and querying.
  • Portable knowledge bases as compressed archives (.tar.gz).
  • Local-first architecture; no data sent to external services.
  • Extensible pipeline system for text, documents, audio, images, and video.

Use cases of Embedding MCP Server

  • Semantic search across document collections using meaning-based queries.
  • Building and querying knowledge graphs from unstructured text data.
  • Running AI-driven text processing pipelines (summarization, extraction).
  • Sharing portable knowledge bases as .tar.gz archives between systems.

よくある質問

1

What is txtai and why is it used?

txtai is an all-in-one embeddings database for RAG that provides semantic search, knowledge graph construction, and language model workflows. This server leverages txtai as its core embedding and search engine.

2

What are the runtime requirements?

Python 3.10 or newer is recommended. Installation can be done via `uv`, `pip`, or `conda`. The server runs locally and does not require connection to external services.

3

Where does the knowledge base data live?

Knowledge bases are stored locally on the file system—either as plain folders or as portable `.tar.gz` archives. The server loads them from a path specified via the `--embeddings` argument.

4

What transports are supported?

The server supports both `sse` and `stdio` transports, configurable with the `--transport` argument. The default transport is `stdio`.

5

Is authentication or authorization supported?

The README does not mention any authentication or authorization mechanisms for the server.

コメント

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