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:

Purchase the Lindorm wide-table engine, search-engine, vector-engine, and AI-engine on Alibaba Cloud.
Deploy a text-embedding model by following the official instructions.
Create your index (knowledgebase) and import your data using the deployed embedding model.

Environment Setup

Clone this repository and navigate to the project directory.
Create your environment file:

cp .env.example .env

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

Install the Cline extension.
Create the .env file under /path/to/alibabacloud-lindorm-mcp-server/
Copy the MCP configuration from .vscode/mcp.json to cline_mcp_settings.json, replacing paths and variables as needed.
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

Configuration on lindorm

To utilize this MCP server, follow these steps:

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

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

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

Environment Setup

Clone this repository and navigate to the project directory.

Create your environment file:

cp .env.example .env

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.

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