Skip to main content
Glama

base_tablePreview

Preview data samples and table structure from Teradata databases to verify content and schema before analysis or querying.

Instructions

This function returns data sample and inferred structure from a database table or view via SQLAlchemy, bind parameters if provided (prepared SQL), and return the fully rendered SQL (with literals) in metadata.

Arguments: table_name - table or view name database_name - Database name

Returns: ResponseType: formatted response with query results + metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYes
database_nameNo

Implementation Reference

  • The handler function that implements the core logic of the base_tablePreview tool. It executes 'SELECT TOP 5 * FROM table' to fetch a sample of data, extracts column information, builds metadata, and returns a formatted response using create_response.
    def handle_base_tablePreview(conn: TeradataConnection, table_name: str, database_name: str | None = None, *args, **kwargs): """ This function returns data sample and inferred structure from a database table or view via SQLAlchemy, bind parameters if provided (prepared SQL), and return the fully rendered SQL (with literals) in metadata. Arguments: table_name - table or view name database_name - Database name Returns: ResponseType: formatted response with query results + metadata """ logger.debug(f"Tool: handle_base_tablePreview: Args: tablename: {table_name}, databasename: {database_name}") if database_name is not None: table_name = f"{database_name}.{table_name}" with conn.cursor() as cur: cur.execute(f'select top 5 * from {table_name}') columns = cur.description sample = rows_to_json(cur.description, cur.fetchall()) metadata = { "tool_name": "base_tablePreview", "database": database_name, "table_name": table_name, "columns": [ { "name": c[0], "type": c[1].__name__ if hasattr(c[1], '__name__') else str(c[1]), "length": c[3] } for c in columns ], "sample_size": len(sample) } logger.debug(f"Tool: handle_base_tablePreview: metadata: {metadata}") return create_response(sample, metadata)
  • Dynamic registration loop that discovers all 'handle_*' functions from loaded tool modules and registers them as MCP tools. The tool 'base_tablePreview' is registered from 'handle_base_tablePreview' by stripping the 'handle_' prefix and using the function's docstring as description.
    all_functions = module_loader.get_all_functions() for name, func in all_functions.items(): if not (inspect.isfunction(func) and name.startswith("handle_")): continue tool_name = name[len("handle_"):] if not any(re.match(p, tool_name) for p in config.get('tool', [])): continue wrapped = make_tool_wrapper(func) mcp.tool(name=tool_name, description=wrapped.__doc__)(wrapped) logger.info(f"Created tool: {tool_name}")

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/blitzstermayank/MCP'

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