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sdebruyn

fabric-dw-mcp-cli

by sdebruyn

create_empty_table

Create an empty table in a Fabric Data Warehouse by specifying column name, SQL type, and nullability. Generates a DDL CREATE TABLE statement without inserting data.

Instructions

Create an empty table from an explicit column spec (DDL only, no data).

Builds CREATE TABLE [schema].[table] (col TYPE [NULL|NOT NULL], …) from the supplied column definitions. No data is read or inserted; this is a pure DDL operation.

Server-side file access is unreliable in MCP deployments, so CSV/Parquet inference is not available via this tool — use the fabric-dw tables create --from-parquet or --from-csv CLI commands instead.

Only supported on Fabric Data Warehouses (not SQL Analytics Endpoints).

Args: workspace: Workspace name or GUID. item: Warehouse name or GUID. qualified_name: Dot-separated qualified table name, e.g. dbo.sales. columns: List of column definitions, each a dict with: name (str) — column identifier; sql_type (str) — Fabric-DW T-SQL type, e.g. "INT", "VARCHAR(255)"; nullable (bool, optional, default true) — whether the column allows NULL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceYes
itemYes
qualified_nameYes
columnsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must cover behavioral aspects. It states it is pure DDL with no data read/inserted, but lacks details on validation, error handling, permissions, or whether it checks for existing tables. Could be more comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear paragraphs and a bulleted args list. Front-loaded with primary purpose. Every sentence earns its place; no extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no annotations, the description covers the tool's purpose, limitations, and all parameter details. It has output schema (indicated true) and provides sufficient context for a DDL tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description provides detailed argument explanations, including workspace, item, qualified_name, and columns with explicit fields (name, sql_type, nullable). This compensates for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates an empty table from explicit column specs (DDL only, no data). It distinguishes from siblings like clone_table and create_table by emphasizing it is pure DDL without data. Also specifies it is only for Fabric Data Warehouses, not SQL Analytics Endpoints.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (creating empty tables via DDL) and when not to use (CSV/Parquet inference not available; directs to CLI commands instead). Also mentions server-side file access unreliability, guiding users away from relying on it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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