create_table

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

With no annotations, the description carries the full behavioral disclosure burden and does so thoroughly. It reveals that select_body is executed verbatim as DDL, that the guard is fail-closed and rejects write keywords and semicolons even inside literals, and that column existence is not validated for CTAS. It also explains the exact DDL transformation when cluster_by is supplied.

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

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

The description is long but every section earns its place given the high-risk DDL execution. The key caution is front-loaded early, followed by the guard mechanics and then the parameter details. The warning about write keywords is repeated briefly in Args but that repetition reinforces a safety-critical point rather than wasting space.

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

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

The tool combines DDL execution, platform restrictions, and a security guard, all of which are disclosed. The description explains the CTAS behavior, the fail-closed validation, the endpoint rejection, the cluster_by DDL shape, and the column validation caveat. Since an output schema exists, return-value details are not required, and nothing essential is missing.

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

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

Schema description coverage is 0%, so the description must compensate for the bare input schema, and it does. Each parameter (workspace, item, qualified_name, select_body, cluster_by) gets a meaningful explanation, including the example for qualified_name, the single-read-only-SELECT constraint, and the cluster_by limit of 4 columns.

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

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

The description states a specific verb and resource: 'Create a new SQL table via CTAS (CREATE TABLE AS SELECT).' It clearly distinguishes this from siblings like create_empty_table by emphasizing the SELECT-based creation method. The Fabric-specific scope further sharpens what the tool does.

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

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

The description explicitly states where the tool is supported (Fabric Data Warehouses) and where it is not (SQL Analytics Endpoints), including the resulting ToolError behavior. It also explains when select_body is acceptable and warns about the guard, giving clear context for use. It does not name a specific alternative tool for non-CTAS table creation, but the exclusion is clear enough.

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