MCP Server: Weaviate MCP Server

Overview: What is Weaviate MCP Server?

The Weaviate MCP Server is an essential component in connecting AI applications such as Claude Desktop, Continue, Cursor, and others to a specific data source—Weaviate—through the Model Context Protocol (MCP). This server acts as a bridge between the AI application's query requests and the underlying data storage system, enabling seamless integration of diverse services into the application flow. By adhering to the MCP standards, it ensures compatibility with a wide range of AI tools while enhancing their functionality.

🔧 Core Features & MCP Capabilities

The Weaviate MCP Server is designed to leverage the flexibility and scalability offered by Weaviate's data storage framework. Key features include:

• Real-time Data Synchronization: The server continuously aligns the state between the application and the stored data in Weaviate.
• Modular Architecture: It supports dynamic configuration, allowing users to switch or extend functionalities without downtime.
• Security Enhancements: Implementing robust security measures ensures secure transmission of sensitive data and API keys.

These capabilities make it a robust solution for deploying AI applications that require real-world data integration.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the Weaviate MCP Server is built upon well-established principles of modularity and adaptability. The core protocol implementation involves:

• MCP Client Integration: Each client application, such as Claude Desktop or Continue, interacts with the server through predefined methods.
• Data Handling: Weaviate's sophisticated data retrieval mechanisms are leveraged to ensure efficient query processing.

Mermaid Diagram of MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Server]
    B --> C[Weaviate Data Source]
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8

Mermaid Diagram of Data Architecture

graph TD
    subgraph "Data Flow"
        A[User Query] -->|MCP| B[MCP Server]
        B -->|Weaviate API| C[Weaviate Store]
    end
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8

🚀 Getting Started with Installation

Prerequisites

Before installing the Weaviate MCP Server, ensure you have the following:

• uv installed (refer to the docs for detailed instructions)
• The repository cloned locally

Installation Steps:

1. Install Development/Unpublished Servers Configuration:

   • For macOS, navigate to ~/Library/Application\ Support/Claude/claude_desktop_config.json
   • For Windows, locate %APPDATA%/Claude/claude_desktop_config.json

2. Configure the MCP Server:

   {
     "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"
         }
       }
     }
   }
   

3. Open the Configuration File:

   • Locate and open claude_desktop_config.json
   • Insert or modify the above code snippet into the file

4. Save and Restart the Application:

   • Save your changes
   • Restart Claude Desktop for the new configuration to take effect

💡 Key Use Cases in AI Workflows

Use Case 1: Real-Time Chatbot Integration

Scenario:

A chatbot powered by an AI application needs real-time data from a Weaviate database to provide context-aware responses.

Technical Implementation:

• The server configures the MCP client to connect with Weaviate.
• Queries are sent through the MCP protocol, retrieved from Weaviate, and used to enhance the bot's response logic.

Use Case 2: Personalized Content Recommendations

Scenario:

An application aims to provide personalized recommendations based on user interactions stored in a Weaviate database.

Technical Implementation:

• User behavior data is stored in Weaviate.
• The MCP client retrieves this information and uses it within the application's recommendation engine, ensuring highly relevant content suggestions.

🔌 Integration with MCP Clients

The Weaviate MCP Server supports seamless integration with AI clients such as Claude Desktop (macOS & Windows), Continue, and Cursor. Below is a compatibility matrix detailing the current support status:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

📊 Performance & Compatibility Matrix

The performance and compatibility of the Weaviate MCP Server are crucial for ensuring reliable AI application flows. Here's a summary:

• Compatibility: Fully compatible with all listed clients, providing stable performance.
• Scalability: Supports large-scale data processing with Weaviate’s robust backend.

🛠️ Advanced Configuration & Security

Advanced users can fine-tune the server configuration for optimal performance. Key areas include:

• Security Measures: Implementing HTTPS and secure API keys management.
• Logging and Error Handling: Enhanced logging for debugging issues and implementing error handling for resilience.

Mermaid Diagram of MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Server]
    B --> C[Weaviate Data Source]
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8

Configuration Sample

{
  "mcpServers": {
    "weaviate-mcp-server": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-weaviate"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

❓ Frequently Asked Questions (FAQ)

1. Q: How do I configure the Weaviate MCP Server for my AI application?

   • A: You need to modify the mcpServers section in your claude_desktop_config.json, as shown in the configuration sample.

2. Q: What are the supported clients currently by this server?

   • A: The server supports full compatibility with Claude Desktop, Continue, and Cursor for tools integration.

3. Q: Can I use the Weaviate MCP Server with other AI frameworks besides those mentioned here?

   • A: While the server is optimized for the listed clients, you might be able to adapt it for other compatible frameworks following similar configurations.

4. Q: How do I ensure the security of my data when using this server?

   • A: Implementing HTTPS and secure API key management are recommended best practices.

5. Q: What are some real-world use cases where this MCP server would be beneficial?

   • A: Real-time chatbot integration and personalized content recommendations are prime examples.

👨‍💻 Development & Contribution Guidelines

Contributions to the Weaviate MCP Server are welcome. To contribute, follow these steps:

• Fork the repository.
• Submit a pull request with detailed explanations of changes made.

🌐 MCP Ecosystem & Resources

For more information and resources related to MCP Protocol and its usage in AI applications, refer to the official documentation and community forums available on:

• Model Context Protocol Documentation
• Weaviate Documentation
• Claude Desktop Integration Guide

By leveraging the power of Weaviate MCP Server, developers can enhance their AI applications with rich contextual data and robust tools integration.