Weaviate MCP Server

Overview

The Weaviate MCP Server is a lightweight bridge that exposes the full capabilities of the Weaviate vector database to AI assistants via the Model Context Protocol (MCP). By turning Weaviate into an MCP-compliant service, developers can seamlessly integrate vector search, schema management, and data ingestion into conversational agents such as Claude or other AI assistants that understand MCP. This eliminates the need for custom SDKs or REST wrappers, allowing an assistant to query and modify a knowledge base in real time using the same declarative language that it uses for prompts.

The server solves a common pain point in AI‑powered applications: the difficulty of combining unstructured, semantically rich data with dynamic conversational logic. Weaviate already offers powerful hybrid search (vector + keyword) and schema flexibility, but interacting with it programmatically requires HTTP calls or client libraries. With the MCP server, an AI assistant can issue high‑level commands—such as “insert this document” or “search for items related to X”—and receive structured responses, all within the same conversation flow. This simplifies architecture, reduces latency, and keeps data access logic centralized in the assistant’s prompt layer.

Key features of the Weaviate MCP Server include:

• Insert One: A straightforward tool that accepts an object definition and writes it to Weaviate, enabling the assistant to add new knowledge items on demand.
• Query: A hybrid search tool that allows keyword filtering and vector similarity scoring, returning ranked results directly to the assistant.
• Schema‑agnostic: The server can work with any Weaviate schema; the assistant supplies the object structure in the request body, and the server forwards it unchanged.
• MCP‑compliant: All tools adhere to the MCP specification, ensuring compatibility with any assistant that supports the protocol.

Typical use cases span a wide range of domains:

• Customer support: An AI assistant can pull the most relevant knowledge‑base articles from Weaviate and present them to users in real time.
• Content recommendation: The assistant can ingest new media items and surface personalized suggestions based on semantic similarity.
• Enterprise search: Teams can query internal documents, code snippets, or policy files stored in Weaviate through conversational prompts.

Integration into AI workflows is straightforward. The assistant’s prompt includes a tool invocation that specifies the desired action (insert or query). The MCP server receives the request, forwards it to Weaviate, and returns a structured JSON response. The assistant then formats the data into natural language or passes it along to downstream tools. Because the server operates over standard HTTP and follows MCP conventions, it can be deployed behind any API gateway or in a serverless environment without additional plumbing.

What sets this MCP server apart is its minimal footprint combined with full Weaviate functionality. Developers can spin it up quickly, and the server’s design ensures that all vector‑search logic remains within Weaviate, preserving performance and scalability. By unifying data access with conversational AI through MCP, the Weaviate MCP Server enables richer, context‑aware interactions that would otherwise require complex custom integrations.