Weaviate MCP Client

Weaviate MCP Client is an MCP server that connects LLM applications directly to a Weaviate vector database instance. Developed for developers and data teams, it bridges Model Context Protocol clients like Claude Desktop with vector storage services to allow intelligent assistants to manage, ingest, and search records. The server provides tools to insert new data objects into Weaviate and run hybrid search operations across existing document collections. By configuring standard API keys and endpoints, users enable their AI agents to dynamically fetch relevant contextual embeddings, execute combined keyword and vector retrieval queries, and store context from ongoing conversations into persistent storage. This functionality supports retrieval-augmented generation and automated semantic indexing workflows, removing the requirement to manually handle vector database connectivity through custom scripts outside the standard MCP environment.

Category: AI Memory & Context

Tags: embeddings, vector-database, vector-search, weaviate

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How to install and configure Weaviate MCP Client

Follow these steps to set up and register the server with your MCP client: 1. Ensure Node.js is installed on your system to run commands via npx. 2. Open your Claude Desktop configuration file (claude_desktop_config.json). 3. Add the weaviate configuration under the mcpServers object: json { "mcpServers": { "weaviate": { "command": "npx", "args": [ "-y", "mcp-weaviate-client" ], "env": { "WEAVIATE_API_KEY": "your_api_key_here" } } } } 4. If using a custom server instance, also set the WEAVIATE_MCP_URL environment variable within the env object. 5. Restart Claude Desktop to start using the tools.

What you can do with Weaviate MCP Client

• Running hybrid search queries across document collections to retrieve contextual information for RAG pipelines during AI conversations. - Inserting newly generated textual content or documents directly into a Weaviate vector database instance via tool calls. - Enabling language models to verify vector database contents and dynamically query specific embedding collections during task execution. - Maintaining persistent external memory and context across chat interactions by storing conversational facts into Weaviate.

Key facts

• Open Source
• https://github.com/braincreator/mcp-weaviate-client
• AI Memory & Context, Databases & Data Stores
• embeddings, vector-database, vector-search, weaviate

Part of MCP Servers

How do I install Weaviate MCP Client?

You can run the server directly using npx by configuring npx -y mcp-weaviate-client as the command and arguments in your Claude Desktop configuration file alongside your API key.

What tools does Weaviate MCP Client provide?

The server exposes two main tools: weaviate-insert-one to insert individual objects into the database, and weaviate-query to run hybrid search queries combining vector and keyword retrieval.

What environment variables are required?

You must provide WEAVIATE_API_KEY for authenticating with the target instance. You may also specify WEAVIATE_MCP_URL if you need to point to a custom server instead of the default endpoint.

Is Weaviate MCP Client open source?

Yes, Weaviate MCP Client is open-source software distributed under the MIT license, with source code available on GitHub.