> For the complete documentation index, see [llms.txt](https://mariadb.com/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mariadb.com/docs/tools/mariadb-enterprise-mcp-server/features.md).

# Features

MariaDB Enterprise MCP Server provides database introspection and SQL tools, vector store and semantic search tools, and RAG orchestration for document ingestion and response generation.

The MariaDB Enterprise MCP Server offers a comprehensive suite of tools, categorized into standard database operations, advanced vector functionalities, and workflow orchestration.

## Standard Database Operations

These tools provide fundamental control and insight into your MariaDB environment. By default, operations are read-only (`MCP_READ_ONLY = true`) but can be configured for write access (`MCP_READ_ONLY = false`).

* `list_databases`: Discovers all accessible databases.
* `list_tables`: Enumerates all tables within a specified database.
* `get_table_schema`: Retrieves the detailed schema for a specific table, including column names, data types, keys, and default values.
* `execute_sql`: Executes read-only SQL queries like `SELECT`, `SHOW`, and `DESCRIBE`. Supports parameterized queries for enhanced security.
* `create_database`: Creates a new database if it does not already exist.

## Harnessing the Power of Vectors: Advanced AI Functionality

The server’s integrated vector functionality enables semantic search and other embedding-based operations directly within your database.

### Vector Store Management

* `create_vector_store`: Creates a new table optimized as a vector store. The schema includes columns for `id`, `document`, `embedding` (VECTOR type), and `metadata` (JSON). Users can specify the embedding model and distance function (e.g., cosine, euclidean) at creation.
* `list_vector_stores`: Lists all tables in a database that are identified as vector stores.
* `delete_vector_store`: Securely removes a vector store table.

### Embedding and Search Operations

* `insert_docs_vector_store`: Inserts documents and associated metadata into a vector store. The server manages the generation of embeddings using a configured service.
* `search_vector_store`: Performs semantic similarity searches by generating an embedding for a user query and finding the 'k' most similar documents in the specified vector store.

## Workflow Orchestration

The server exposes powerful orchestration endpoints that allow an AI agent to execute an entire RAG pipeline through a single, secure interface.

* **Ingestion (`/orchestrate/ingestion`)**: Triggers the ingestion of documents into a specified vector store, including the chunking and embedding processes.
* **Generation (`/orchestrate/generation`)**: Executes a query against a set of documents, performing retrieval and generating a final, context-aware response from an LLM.

***

### Tool Summary

| Tool Name                  | Description                                                                                          | Category                     |
| -------------------------- | ---------------------------------------------------------------------------------------------------- | ---------------------------- |
| `list_databases`           | Discovers all accessible databases.                                                                  | Standard Database Operations |
| `list_tables`              | Enumerates all tables within a specified database.                                                   | Standard Database Operations |
| `get_table_schema`         | Retrieves the detailed schema for a specific table.                                                  | Standard Database Operations |
| `execute_sql`              | Executes read-only SQL queries.                                                                      | Standard Database Operations |
| `create_database`          | Creates a new database if it does not already exist.                                                 | Standard Database Operations |
| `create_vector_store`      | Creates a new table optimized as a vector store.                                                     | Vector & AI Functionality    |
| `list_vector_stores`       | Lists all tables identified as vector stores.                                                        | Vector & AI Functionality    |
| `delete_vector_store`      | Securely removes a vector store table.                                                               | Vector & AI Functionality    |
| `insert_docs_vector_store` | Inserts documents and metadata into a vector store.                                                  | Vector & AI Functionality    |
| `search_vector_store`      | Performs a semantic similarity search on a vector store.                                             | Vector & AI Functionality    |
| `rag_ingestion`            | Triggers the full document ingestion pipeline.                                                       | Workflow Orchestration       |
| `rag_generation`           | Synthesizes retrieved information with the user's query to generate a final, context-aware response. | Workflow Orchestration       |

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