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redesignhealth

Google Workspace MCP Server

debug_table_structure

Inspect table dimensions, cell positions, and contents in Google Docs to diagnose and fix table 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

Args: user_google_email: User's Google email address document_id: ID of the document to inspect table_index: Which table to debug (0 = first table, 1 = second table, etc.)

Returns: str: Detailed JSON structure showing table layout, cell positions, and current content

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
document_idYes
table_indexNo
user_google_emailYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 thoroughly explains the output format (JSON with dimensions, positions, content, insertion indices) and how to interpret it. However, it does not explicitly state non-destructive behavior or error handling, which is a minor gap for an inspection tool.

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 lengthy but well-structured with sections and bullet points. It is front-loaded with a clear 'ESSENTIAL' headline, and each section adds practical value. Some repetition occurs but overall it is organized and readable.

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 covers purpose, usage triggers, output decoding, and workflow integration, making it highly complete for a debugging tool. Even with an output schema present, it clearly explains the return format and interpretation, leaving little ambiguity.

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

Parameters5/5

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

Schema coverage is 0%, but the description includes an Args section that explains all three parameters in user-meaningful terms (e.g., 'table_index: Which table to debug'). This fully compensates for the schema's minimal type-only information.

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: to debug table structure by showing dimensions, cell positions, content, and insertion indices. It uses specific verbs like 'debug' and 'shows', and clearly distinguishes itself from sibling tools by focusing on table-specific inspection.

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 when-to-use scenarios (e.g., wrong cell placement, 'table not found' errors, planning to use populate_existing_table) and workflow integration steps. This gives clear guidance on when to choose this tool over 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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