update_column

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

With no annotations, the description carries the full behavioral burden and exceeds it: it discloses that only provided fields change, every stored row is revalidated, invalid rows are preserved and reported with errors, and renaming moves values. It also documents success and error return shapes.

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 front-loaded with purpose and side effects, then organized into compact Args, Returns, and Example sections. Every sentence adds useful information; the example is illustrative rather than redundant.

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?

For a mutation tool with no annotations and no parameter descriptions in the schema, this description is complete: it explains side effects, validation behavior, error cases, return contract, and invocation via a realistic example. An agent has everything needed to call it correctly.

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?

The input schema has 0% description coverage, but the Args section fully compensates: dataset is defined as an exact name, column as the current column name, and changes is illustrated with concrete examples like {'max_value': 10} and {'name': 'full_name'}. The example call reinforces the parameter shapes.

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 opening sentence states a specific verb ('Change'), a concrete resource ('a column'), and the scope ('existing dataset'). Naming 'name, type, or constraints' clearly distinguishes it from sibling tools like add_column and remove_column.

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 clearly establishes that this is for modifying an existing column and requires the current column name. However, it does not explicitly name alternatives or say 'use add_column instead when adding a new column', so it stops short of full routing guidance.

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