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Debug Table Structure

debug_table_structure
Read-onlyIdempotent

Inspect table dimensions, cell positions, content, and insertion indices to debug data placement issues in Google Docs.

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?

Annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral details beyond annotations by explaining what the tool reveals (dimensions, cell coordinates, content, insertion indices). It also aligns perfectly with annotations, with no contradiction.

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 labeled sections (ESSENTIAL DEBUGGING TOOL, USE THIS IMMEDIATELY WHEN, WHAT THIS SHOWS YOU, HOW TO READ THE OUTPUT, WORKFLOW INTEGRATION). While it is relatively long, every sentence adds value and the structure aids readability. Slightly verbose but not excessive.

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?

Given an output schema exists, the description compensates by explaining how to interpret the output (dimensions, position, current_content, insertion_index). It covers common scenarios and integrates with other tools (populate_existing_table). Annotations provide safety guarantees, making the tool fully described.

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?

The input schema has 100% coverage with descriptions for all three parameters. The description does not add further semantic information about the parameters beyond what is already in the schema. Baseline score of 3 is appropriate.

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 tool's purpose: debugging table structure. It uses specific verbs like 'inspect' and 'debug', and explicitly lists conditions when it should be used, distinguishing it from sibling tools that deal with document structure or sheet tables.

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?

The description provides explicit guidelines on when to use the tool: immediately when tables don't work, for planning before populating, and for debugging failures. It also integrates with a workflow (After creating, Before populating, After population fails, When debugging), leaving no ambiguity about 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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