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dltHub-AI-workbench

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by dlt-hub

get_table_create_sql

Retrieve the CREATE TABLE DDL statement for a specified table in your destination's SQL dialect. Use pipeline name and table name to generate the exact SQL needed.

Instructions

Get CREATE TABLE DDL for the table in the destination's SQL dialect.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYes
pipeline_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as whether the tool is read-only (likely safe), requires specific permissions, or has side effects. The description is minimal and lacks transparency beyond the basic action.

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

Conciseness3/5

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

The description is a single sentence, which is concise but at the expense of missing important details like parameter explanations. For a tool with two parameters, it could be slightly expanded to include parameter context while remaining short. It is front-loaded with the action but lacks completeness.

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

Completeness3/5

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

Given the low complexity (2 required params, no nested objects), the description provides some context (destination SQL dialect) but does not explain the output or how the DDL relates to the pipeline. While an output schema exists, the description could still mention that the result is a SQL DDL statement. It is adequate but not fully comprehensive.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not add meaning to the two parameters. It does not explain valid values for 'pipeline_name' or 'table_name', nor does it clarify if 'table_name' should include a schema prefix. The description fails to compensate for the lack of schema documentation.

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 specifies the action ('Get'), the resource ('CREATE TABLE DDL'), and the context ('for the table in the destination's SQL dialect'). It distinguishes from siblings like 'get_table_schema' (which returns schema structure, not DDL) and 'execute_sql_query' (which runs arbitrary SQL).

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or contexts. For example, it does not indicate that this tool is ideal for obtaining the exact DDL for replication or migration purposes, or when 'get_table_schema' might be more appropriate.

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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