MCP Server Weaviate

@sndani

代码仓库

基本信息

分类

数据库

许可证

GPL-3.0

传输方式

stdio

作者

sndani

关于 MCP Server Weaviate

暂无概览

MCP Server Weaviate 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-localhost-server-weaviate": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@weaviate/mcp-server-weaviate",
        "--client",
        "claude"
      ]
    }
  }
}

MCP Server Weaviate 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。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is mcp-server-weaviate?

mcp-server-weaviate is an MCP (Model Context Protocol) server that connects AI agents to Weaviate, a vector database. It enables tools for searching and storing vector data, using OpenAI embeddings, and is designed for developers building AI-powered applications with vector search.

How to use mcp-server-weaviate?

Install via Smithery with npx -y @smithery/cli install @weaviate/mcp-server-weaviate --client claude, or manually configure Claude Desktop by adding a JSON entry to claude_desktop_config.json. The configuration requires --weaviate-url, --weaviate-api-key, --search-collection-name, --store-collection-name, and --openai-api-key arguments.

Key features of mcp-server-weaviate

Connects to Weaviate vector database for AI agents
Provides search and store tools for vector data
Uses OpenAI embeddings for vector operations
Supports configurable search and store collections
Easy installation via Smithery or manual configuration

Use cases of mcp-server-weaviate

Semantic search over large document collections
Storing and retrieving vector embeddings for LLM context
Building AI assistants that query Weaviate databases
Enabling vector-based retrieval for RAG pipelines

常见问题

How do I install mcp-server-weaviate?

Install via Smithery using `npx -y @smithery/cli install @weaviate/mcp-server-weaviate --client claude`, or manually configure Claude Desktop with the provided JSON config.

What are the prerequisites for using mcp-server-weaviate?

You need to have `uv` installed, clone the repository, and have a Weaviate instance with an API key. An OpenAI API key is also required for embeddings.

What configuration parameters are required?

The server requires `--weaviate-url`, `--weaviate-api-key`, `--search-collection-name`, `--store-collection-name`, and `--openai-api-key`. These are passed as command-line arguments.

Do I need separate collections for search and store?

Yes, the configuration expects separate collection names for search and store operations, specified via `--search-collection-name` and `--store-collection-name`.

How does mcp-server-weaviate use OpenAI?

The server uses an OpenAI API key to generate vector embeddings for both search and store operations, enabling semantic search capabilities.

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