Skip to main content
Glama
Teradata

Teradata MCP Server

Official
by Teradata

tmpl_nameOfTool

Analyze and process arguments to generate formatted query results with metadata, integrated within the Teradata MCP Server for database management and SQL execution.

Instructions

Arguments: arguments - arguments to analyze

Returns: ResponseType: formatted response with query results + metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argumentYes

Implementation Reference

  • Handler function for the tmpl_nameOfTool tool. This is a template implementation that executes placeholder Teradata queries based on an optional string argument and returns JSON-formatted results with metadata.
    def handle_tmpl_nameOfTool(conn: TeradataConnection, argument: str | None, *args, **kwargs): """ <description of what the tool is for> Arguments: arguments - arguments to analyze Returns: ResponseType: formatted response with query results + metadata """ logger.debug(f"Tool: handle_tmpl_nameOfTool: Args: argument: {argument}") with conn.cursor() as cur: if argument == "": logger.debug("No argument provided") rows = cur.execute("Teradata query goes here;") else: logger.debug(f"Argument provided: {argument}") rows = cur.execute(f"Teradata query goes here with argument {argument};") data = rows_to_json(cur.description, rows.fetchall()) metadata = { "tool_name": "tmpl_nameOfTool", "argument": argument, "rows": len(data) } logger.debug(f"Tool: handle_tmpl_nameOfTool: metadata: {metadata}") return create_response(data, metadata)

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Teradata/teradata-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server