Lindorm MCP Server

This repository is an example of how to create a MCP server for Lindorm, a multi-model NoSQL database.

Usage

Configuration on lindorm

To utilize this MCP server, follow these steps:

1. Purchase the Lindorm wide-table engine, search-engine, vector-engine, and AI-engine on Alibaba Cloud.

2. Deploy a text-embedding model by following the official instructions.

3. Create your index (knowledgebase) and import your data using the deployed embedding model.

Related MCP server: Alibaba Cloud RDS OpenAPI MCP Server

Environment Setup

1. Clone this repository and navigate to the project directory.

2. Create your environment file:

cp .env.example .env

3. Edit the .env file with your specific configuration:

• LINDORM_INSTANCE_ID: Your Lindorm instance ID

• USING_VPC_NETWORK: Set to true if running on VPC network, otherwise false

• USERNAME: Your Lindorm account username

• PASSWORD: Your Lindorm account password

• TEXT_EMBEDDING_MODEL: The name of your deployed text-embedding model

• TABLE_DATABASE: The database for SQL operations Note: This configuration assumes all engines share the same username and password.

Running the MCP Server

You should install uv. Directly start the mcp server.

cd /path/to/alibabacloud-lindorm-mcp-server/

uv pip install .

uv run python -m src.lindorm_mcp_server.server

Visual Studio Code

1. Install the Cline extension.

2. Create the .env file under /path/to/alibabacloud-lindorm-mcp-server/

3. Copy the MCP configuration from .vscode/mcp.json to cline_mcp_settings.json, replacing paths and variables as needed.

4. Start the MCP server through the Cline extension.

Components

• LindormVectorSearchClient: Performs full-text and vector searches on the search and vector engines.

• LindormWideTableClient: Executes SQL operations on Lindorm wide tables.

Available Tools

• lindorm_retrieve_from_index: Retrieve from an existing indexes(or knowledgebase) using both full-text search and vector search, and return the aggregated results

  • Parameters

    • index_name: the index name, or known as knowledgebase name

    • query: the query that you want to search in knowledgebase

    • content_field: the text field that store the content text. You can get it from the index structure by lindorm_get_index_mappings tool

    • vector_field: the vector field that store the vector index. You can get it from the index structure by lindorm_get_index_mappings tool

    • top_k: the result number that you want to return

• lindorm_get_index_fields: Get the fields info of the indexes(or knowledgebase), especially get the vector stored field and content stored field.

  • Parameters:

    • index_name: the index name, or known as knowledgebase name

• lindorm_list_all_index: List all the indexes(or knowledgebase) you have.

• lindorm_execute_sql: Execute SQL query on Lindorm database.

  • Parameters

    • query: The SQL query to execute which start with select

• lindorm_show_tables: Get all tables in the Lindorm database

• lindorm_describe_table: Get tables schema in the Lindorm database

  • Parameters

    • table_name: the table name