mcp-server-weaviate

@sndani

About mcp-server-weaviate

No overview available yet

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"
      ]
    }
  }
}

Tools

No tools detected

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

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.

Frequently asked questions

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.

Comments

Basic information

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