Embedding MCP Server

@Geeksfino

代码仓库

基本信息

分类

记忆与知识

许可证

MIT

传输方式

stdio

作者

Geeksfino

关于 Embedding MCP Server

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

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

Embedding MCP 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。

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 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.

常见问题

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.

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.

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.

What transports are supported?

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

Is authentication or authorization supported?

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

记忆与知识分类下的更多 MCP 服务器

Memory

modelcontextprotocol

Model Context Protocol Servers

88.1K

HandbookHub MCP

veryayskiy

Search saved employee handbooks from your AI assistant. Four read-only tools list authorized handbooks, browse policies, search content, and read source pages with citation links. Uses scoped bearer tokens and Streamable

Neither MCP

stonianua

Project context your AI can query through MCP — with source evidence.

Sensefold

SenseFold Labs

Personal context for every AI: search, read, and write back to your private Markdown library of articles, threads, PDFs, notes, and captured ChatGPT/Claude/Gemini/Grok conversations. Remote server, OAuth 2.1 paste-and-au

ctxfile

Local-first MCP server that snapshots your project's working state into one context object. Open-core, privacy-first.