MariaDB Vector MCP Server

Overview

The MariaDB Vector MCP server turns a MariaDB database with vector capabilities into an AI‑ready backend that can be queried by any Model Context Protocol (MCP) client. By exposing a set of MCP tools for vector store and document management, the server gives LLM agents a natural‑language interface to both persist conversational data and retrieve contextual information from a knowledge base. This eliminates the need for custom API wrappers or manual database queries, allowing developers to focus on building higher‑level agent logic.

At its core, the server solves the problem of bridging relational data with semantic search. MariaDB’s vector extension stores dense embeddings alongside traditional tables, enabling fast similarity queries. The MCP server abstracts the technical details—creating vector stores, inserting documents with metadata, and executing semantic searches—into simple tool calls that any MCP‑compatible client can invoke. This is especially valuable for developers who want to add persistent memory or knowledge retrieval to their agents without writing database code.

Key capabilities include:

• Vector store lifecycle management – Create, delete, and list vector stores with dedicated MCP tools (, , ).
• Document ingestion – Add arbitrary text documents, optionally enriched with metadata, into a chosen vector store ().
• Semantic retrieval – Execute similarity searches against a vector store, returning the most relevant documents ().
• Embedding integration – Automatically generate embeddings for incoming documents using an OpenAI model, configurable via environment variables (, ).

These features let developers build use cases such as knowledge‑base powered chatbots, conversational search assistants, or persistent memory systems where past interactions are stored and later retrieved to inform new responses. In a typical workflow, an LLM agent receives a user query, calls to fetch context, and then generates an answer that can be stored back with for future reference.

The server’s design aligns neatly with existing AI toolchains. It can be launched as a lightweight Python package or a Docker container, making it easy to integrate into continuous‑integration pipelines or cloud deployments. Because it follows the MCP specification, any client—Claude Desktop, Cursor/Windsurf, LangGraph, or PydanticAI—can discover and invoke its tools automatically. This interoperability removes vendor lock‑in and accelerates prototyping of multimodal agents that need reliable, fast semantic search backed by a production‑grade database.