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MCP Server Weaviate

@sndani

Repository

Basic information

Category

Databases

License

GPL-3.0

Transports

stdio

Author

sndani

About MCP Server Weaviate

No overview available yet

MCP Server Weaviate config

Add this server to your MCP-compatible client using the configuration below.

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

MCP Server Weaviate tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

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

Frequently asked questions

1

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.

2

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.

3

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.

4

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`.

5

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.

Comments

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