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IBM watsonx.data MCP Server

Official
by IBM

rename_column

Rename a column in a watsonx.data table by specifying catalog, schema, table, current name, new name, and engine ID.

Instructions

Rename a column in a table in a watsonx.data schema.

Args: catalog_name: Catalog containing the table (e.g., "iceberg_data") schema_name: Schema containing the table table_name: Table containing the column column_name: Current column name new_column_name: New name for the column engine_id: Engine ID to use for the operation (from list_engines)

Returns: Dict with column details including the new name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
catalog_nameYes
schema_nameYes
table_nameYes
column_nameYes
new_column_nameYes
engine_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description must address behavioral traits. It describes the operation and return value but does not disclose side effects, permissions needed, reversibility, or error conditions. It implies a rename but lacks detail on what happens to dependent objects.

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 with Args and Returns sections, using clear parameter names and brief explanations. Every sentence adds value, and the format is easy to parse for an AI agent.

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 simple operation and presence of an output schema (not shown), the description adequately covers the task. It specifies required parameters and the return type. However, it could add more detail on validation or failure cases.

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

Parameters4/5

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

Schema coverage is 0%, so the description compensates by listing parameters with examples (e.g., catalog_name: 'iceberg_data') and linking engine_id to list_engines. However, it does not explain constraints on new_column_name or behavior if column doesn't exist.

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 operation: 'Rename a column in a table in a watsonx.data schema.' The verb 'rename' and resource 'column' are specific. It distinguishes from sibling 'rename_table' which renames a table, and other table/column operations like 'add_columns'.

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?

No explicit guidance is provided on when to use or not use this tool. There is no mention of prerequisites, alternatives, or contexts where renaming might be inappropriate. The description only lists parameters without usage context.

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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