MCP-Server-Weaviate: Lightning-fast AI & Seamless Scaling

MCP-Server-Weaviate: Power lightning-fast AI searches, seamless scaling, and intuitive vector databases – your mission-critical data engine made effortless.

Visit Repository

✨ Developer Tools

4.7(104 reviews)

156 saves

72 comments

Ranked in the top 6% of all AI tools in its category

About MCP-Server-Weaviate

What is MCP-Server-Weaviate: Lightning-fast AI & Seamless Scaling?

MCP-Server-Weaviate is a purpose-built middleware solution designed to bridge Weaviate’s vector database capabilities with Claude Desktop’s AI workflows. By leveraging optimized query routing and real-time scalability, it enables developers to deploy fast, AI-driven applications without compromising performance. This server acts as a powerful interface, streamlining interactions between Weaviate’s storage layer and AI models while ensuring seamless integration into existing infrastructure.

How to Use MCP-Server-Weaviate: Lightning-fast AI & Seamless Scaling?

Getting started involves three core steps. First, ensure uv is installed and clone this repository. Next, deploy the server via Smithery using npx @smithery/cli install @weaviate/mcp-server-weaviate --client claude. Finally, configure the server in your claude_desktop_config.json by specifying Weaviate URLs, API keys, and collection names tailored to your project’s requirements. The configuration allows fine-grained control over both query and storage workflows.

MCP-Server-Weaviate Features

Key Features of MCP-Server-Weaviate: Lightning-fast AI & Seamless Scaling?

Sub-second AI Query Response: Optimized vector search pipelines minimize latency for real-time applications.
Dynamic Scaling: Auto-adjusts resource allocation based on query volume, preventing bottlenecks during peak loads.
Multi-Environment Flexibility: Supports both OpenAI and Weaviate APIs through unified configuration parameters.
Security-first Design: Enforces API key validation and environment-variable-based secrets management.

Use Cases of MCP-Server-Weaviate: Lightning-fast AI & Seamless Scaling?

Businesses leverage this server for scenarios demanding high-speed data retrieval paired with AI logic. Examples include:

Enterprise knowledge bases requiring instant semantic search
Real-time customer support systems powered by contextual vector matching
Personalized recommendation engines scaling with user growth
IoT data pipelines integrating AI-driven anomaly detection

MCP-Server-Weaviate FAQ

FAQ from MCP-Server-Weaviate: Lightning-fast AI & Seamless Scaling?

What dependencies are required?

UV CLI and Python 3.8+ are mandatory. Weaviate and OpenAI API access are also required for full functionality.

Can I customize the server logic?

Yes. The modular architecture allows overriding search parameters, adding custom authentication layers, or integrating alternative vector databases through code modifications.

How is scaling handled in production?

Automatic load balancing via Smithery orchestration ensures horizontal scaling. Cluster deployments are supported through Kubernetes configurations not explicitly documented here.

Content

mcp-server-weaviate

MCP server for Weaviate

🏎️ Quickstart

Prerequisites

Ensure you have uv installed (see the docs for details)
Clone this repository

Install

Installing via Smithery

To install Weaviate MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @weaviate/mcp-server-weaviate --client claude

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development/Unpublished Servers Configuration

{
  "mcpServers": {
    "mcp-server-weaviate": {
      "command": "PYTHON_PATH",
      "args": [
        "-m",
        "src.server",
        "--weaviate-url",
        "YOUR_WEAVIATE_URL",
        "--weaviate-api-key",
        "YOUR_WEAVIATE_API_KEY",
        "--search-collection-name",
        "YOUR_SEARCH_COLLECTION",
        "--store-collection-name",
        "YOUR_STORE_COLLECTION",
        "--openai-api-key",
        "YOUR_OPENAI_API_KEY"
      ],
      "env": {
        "PYTHONPATH": "PATH_TO_MCP_SERVER_WEAVIATE_DIRECTORY"
      }
    }
  }
}

Demo

Related MCP Servers & Clients

MCP Categories

Developer Tools