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blitzstermayank

Teradata MCP Server

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}")

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