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mcp-server-weaviate

@sndani

代码仓库

关于 mcp-server-weaviate

暂无概览

基本信息

分类

数据库

许可证

GPL-3.0

运行时

python

传输方式

stdio

发布者

sndani

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"
      ]
    }
  }
}

工具

未检测到工具

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 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

FAQ from mcp-server-weaviate

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.

评论

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