home / mcp / lindorm mcp server
Provides MCP access to Lindorm SQL and vector search for knowledge bases and SQL operations.
Configuration
View docs{
"mcpServers": {
"aliyun-alibabacloud-lindorm-mcp-server": {
"command": "uv",
"args": [
"run",
"python",
"-m",
"src.lindorm_mcp_server.server"
],
"env": {
"PASSWORD": "YOUR_LINDORM_PASSWORD",
"USERNAME": "YOUR_LINDORM_USERNAME",
"TABLE_DATABASE": "YOUR_TABLE_DATABASE",
"USING_VPC_NETWORK": "true or false",
"LINDORM_INSTANCE_ID": "YOUR_LINDORM_INSTANCE_ID",
"TEXT_EMBEDDING_MODEL": "YOUR_EMBEDDING_MODEL"
}
}
}
}You deploy and run an MCP server to access Lindorm’s SQL and vector search capabilities through a unified MCP interface. This server lets you perform SQL operations on Lindorm wide tables, as well as text and vector searches, from a single MCP endpoint.
Connect with an MCP client to the Lindorm MCP Server to perform SQL queries against Lindorm wide tables and to run combined full-text and vector searches. You can retrieve index metadata, list available indexes, describe table schemas, and execute SQL queries, all through the MCP endpoints. Use the provided tools to fetch index fields, retrieve documents from an index, and execute SQL against your Lindorm database.
Prerequisites: you need Python available on your machine. You also need the UV runtime interface to start the MCP server.
1. Obtain the server files and navigate to the project directory.
2. Create your environment configuration file from the example.
3. Install and run the MCP server using the runtime commands shown here.
# Step 1: Change to the project directory (adjust path as needed)
cd /path/to/alibabacloud-lindorm-mcp-server/
# Step 2: Create your local environment configuration
cp .env.example .env
# Step 3: Start the MCP server using the UV runtime
uv pip install .
uv run python -m src.lindorm_mcp_server.serverSet up a local environment file with the following values. Replace placeholders with your actual Lindorm details.
LINDORM_INSTANCE_ID=YOUR_LINDORM_INSTANCE_ID
USING_VPC_NETWORK=true|false
USERNAME=YOUR_LINDORM_USERNAME
PASSWORD=YOUR_LINDORM_PASSWORD
TEXT_EMBEDDING_MODEL=YOUR_EMBEDDING_MODEL
TABLE_DATABASE=YOUR_TABLE_DATABASERetrieve from an existing index/knowledgebase using both full-text search and vector search, and return aggregated results. Parameters include index_name, query, content_field, vector_field, and top_k.
Get field information for an index to identify content and vector fields. Parameters: index_name.
List all available indexes/knowledgebases.
Execute a SQL query against the Lindorm database. Parameters: query (SQL string starting with SELECT).
List all tables present in the Lindorm database.
Describe the schema of a specific table. Parameters: table_name.