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debug_table_structure

Inspect table structure in Google Docs: reveal dimensions, cell positions, content, and insertion indices to diagnose population errors.

Instructions

ESSENTIAL DEBUGGING TOOL - Use this whenever tables don't work as expected.

USE THIS IMMEDIATELY WHEN:

  • Table population put data in wrong cells

  • You get "table not found" errors

  • Data appears concatenated in first cell

  • Need to understand existing table structure

  • Planning to use populate_existing_table

WHAT THIS SHOWS YOU:

  • Exact table dimensions (rows × columns)

  • Each cell's position coordinates (row,col)

  • Current content in each cell

  • Insertion indices for each cell

  • Table boundaries and ranges

HOW TO READ THE OUTPUT:

  • "dimensions": "2x3" = 2 rows, 3 columns

  • "position": "(0,0)" = first row, first column

  • "current_content": What's actually in each cell right now

  • "insertion_index": Where new text would be inserted in that cell

WORKFLOW INTEGRATION:

  1. After creating table → Use this to verify structure

  2. Before populating → Use this to plan your data format

  3. After population fails → Use this to see what went wrong

  4. When debugging → Compare your data array to actual table structure

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_google_emailYesUser's Google email address
document_idYesID of the document to inspect
table_indexNoWhich table to debug (0 = first table, 1 = second table, etc.)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully covers behavioral aspects: it explains what the tool shows (dimensions, positions, content, insertion indices) and how to interpret the output, leaving no ambiguity about its non-destructive, read-only nature.

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?

The description is well-structured with headings and bullet points, making it easy to scan. While slightly verbose, it front-loads key information and each section adds value, earning a high score.

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?

The description is fully complete for a debugging tool: it covers when to use, what to expect, how to read output, and integrates into workflow. Presence of output schema further ensures no gaps.

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 coverage is 100%, and the description does not add significant new meaning beyond the schema's parameter descriptions. The baseline of 3 is appropriate because the schema already provides clear definitions.

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 identifies the tool as a debugging utility for tables, listing specific error scenarios and use cases. It distinguishes itself from sibling tools by being diagnostic rather than manipulative.

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

Usage Guidelines5/5

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

Explicitly states when to use the tool (immediately on table issues, after creation, before population, after failure) and references a sibling tool (populate_existing_table) for workflow context, providing clear usage boundaries.

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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