Lindorm MCP Server

# 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](https://common-buy.aliyun.com/?commodityCode=hitsdb_lindormpre_public_cn&regionId=cn-beijing). 2. Deploy a text-embedding model by following the [official instructions](https://help.aliyun.com/zh/lindorm/user-guide/model-management-1). 3. Create your index (knowledgebase) and import your data using the deployed embedding model. ## Environment Setup 1. Clone this repository and navigate to the project directory. 2. Create your environment file: ```shell 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. ```shell 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