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Modify column

modify_column

Rename a custom column or set the same value across all existing job rows, while refusing built-in fields and backing up the file first.

Instructions

Modify a custom column: rename it (new_name) and/or set the same value into every existing job row (fill_value; pass an empty string to clear the column). At least one of new_name / fill_value is required. Refuses on the built-in columns and on a missing column. Backs the file up first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCurrent header text of the column to modify.
new_nameNoNew header text (renames the column).
fill_valueNoIf given, written into this column for every existing job row (empty string clears the column).
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that fill_value writes to every existing row, empty string clears, built-in/missing columns are refused, and a file backup is made first. This is strong behavioral detail for a mutating tool, though it does not mention return values or error formats.

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 compact, front-loaded, and consists of four short sentences, each providing essential operational, constraint, or safety information. There is no filler or redundant repetition of the title.

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?

For a three-parameter mutation tool with no annotations or output schema, the description covers the action, parameter effects, preconditions, refusal cases, and backup behavior. It is sufficiently complete for an agent to select and invoke the tool correctly, even though it does not describe the success response shape.

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?

The input schema already describes all three parameters (100% coverage), so the baseline is 3. The description adds value by specifying the cross-parameter constraint that at least one of new_name or fill_value must be provided, which is not expressed in the schema. Most other parameter meaning is already present in the 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 ('Modify a custom column') and clearly breaks the action into rename and fill operations. It also distinguishes itself from sibling tools like add_column and delete_column by specifying it operates on existing custom columns.

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 gives explicit preconditions: at least one of new_name or fill_value is required, and it refuses on built-in/missing columns. This provides clear context for when the tool should or should not be used, though it does not explicitly name sibling alternatives.

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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