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remove_chooser_row

Destructive

Remove a row from a UChooserTable at a given index or the last row, shrinking all column arrays and results, then compiling and saving the table.

Instructions

Remove one implicit row from a UChooserTable (inverse of add_chooser_row). A chooser row is not a real object: this shrinks EVERY column's RowValues array by one -- at row_index if given, else the last row -- and shrinks ResultsStructs to match, as one undo step, then compiles and saves the table like add_chooser_row; success is false, with save_error, when the save fails. A table with no rows is left as it is (note, saved false). Use remove_chooser_column to drop a column instead.

Returns: { table_path, removed_row_index, previous_row_count, new_row_count, saved, save_error?, note? }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
row_indexNoOptional 0-based row index to remove (default: last row).
table_pathYesLong package path of an existing UChooserTable.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.4/5.0
Behavior5/5

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

Even though annotations already flag destructiveHint=true, the description goes well beyond them: it explains that a chooser row is not a real object, that removal shrinks EVERY column's RowValues array and ResultsStructs, that it is one undo step, that it compiles and saves, and that success is false with save_error on save failure. The no-rows edge case (left as-is, saved false) is also disclosed.

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

Conciseness4/5

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

Front-loads the core action and the inverse relationship, then layers behavioral detail. The middle sentence is long and comma-heavy, but each clause carries distinct, non-redundant information; no filler.

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

Completeness5/5

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

With no output schema present, the description compensates by spelling out the full return shape (table_path, removed_row_index, previous_row_count, new_row_count, saved, save_error?, note?). Combined with the behavioral and failure-mode disclosure, nothing needed to invoke or interpret the call is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so row_index (0-based, default last row) and table_path are already documented, establishing the baseline of 3. The description reinforces the default behavior at the array level but adds little parameter detail beyond 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?

States a specific verb (remove) and resource (one implicit row from a UChooserTable), explicitly frames it as the inverse of add_chooser_row, and draws the boundary against remove_chooser_column. An agent can distinguish it from its closest siblings without opening a schema.

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?

Names the alternative ('Use remove_chooser_column to drop a column instead') and clarifies the inverse relationship to add_chooser_row, giving clear context for when this tool applies. It stops short of a full when/when-not decision rule, but the routing signal is unambiguous.

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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