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sdebruyn

fabric-dw-mcp-cli

by sdebruyn

rename_table

Renames a SQL table in a Microsoft Fabric Data Warehouse using sp_rename. Provide the current qualified table name and a new unqualified name to update it in place without changing schema.

Instructions

Rename a SQL table via sp_rename (Data-Warehouse-only).

Renames the table in-place within the same schema using T-SQL EXEC sp_rename. Both the current qualified name and the new bare name are passed as bound parameters — no SQL injection is possible.

sp_rename cannot move a table to a different schema, so new_name must be an unqualified (bare) name without a dot.

Args: workspace: Workspace name or GUID. item: Warehouse name or GUID. SQL Analytics Endpoints are rejected. qualified_name: Current dot-separated qualified table name, e.g. dbo.sales. new_name: New table name (unqualified, e.g. sales_v2). Must not contain a dot.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemYes
new_nameYes
workspaceYes
qualified_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It explains the in-place same-schema rename, the EXEC sp_rename mechanism, bound parameters preventing SQL injection, and the rejection of SQL Analytics Endpoints. It could mention permissions or side effects on dependent objects, but it covers the most critical behavior.

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?

The description is well-structured and front-loaded, with the core purpose first, then mechanism, then constraints, then a concise Args list. Every sentence adds useful information; there is no filler or redundant restatement of the tool name.

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

Completeness4/5

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

Given the tool has an output schema and no nested objects, the description does not need to explain return values. It fully covers invocation semantics and constraints. The main gap is lack of permission requirements or warnings about dependent objects breaking after rename, which would make it fully complete.

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 description coverage is 0%, but the Args section fully compensates by defining all four parameters with concrete examples and constraints: qualified_name as a dot-separated name, new_name as unqualified without a dot, item as warehouse rejecting SQL Analytics Endpoints, and workspace. This adds substantial meaning beyond the bare schema.

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 opens with a specific verb and resource: 'Rename a SQL table via sp_rename (Data-Warehouse-only).' It clearly distinguishes this from sibling rename tools (rename_warehouse, rename_snapshot, rename_view) by naming the target resource and the underlying mechanism.

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

Usage Guidelines4/5

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

The description provides strong usage context: it is Data-Warehouse-only, cannot move tables across schemas, and rejects SQL Analytics Endpoints. It does not explicitly name alternative tools for cross-schema moves, but the constraints are clear enough to prevent misuse.

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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