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Skeptomenos

google-workspace-mcp-advanced

by Skeptomenos

debug_table_structure

Resolve table population errors by inspecting cell positions, content, and dimensions to verify structure before writing.

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
user_google_emailYes
document_idYes
table_indexNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It thoroughly explains what the tool shows (dimensions, coordinates, content, insertion indices) and how to read the output. It implies a read-only operation but does not explicitly confirm side effects or auth requirements.

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 bold headings, bullet points, and sections. It is front-loaded with urgency and provides valuable detail without redundancy. Slightly verbose but every section earns its place.

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 tool with 3 parameters, no annotations, and an output schema, the description covers purpose, usage, output interpretation, and workflow integration. It lacks error handling details but is otherwise comprehensive.

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?

Parameters are described in the 'Args' section with clear definitions (e.g., 'Which table to debug (0 = first table, etc.)'). Since schema description coverage is 0%, the description compensates by adding meaning beyond type and requirement info.

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 it is an 'ESSENTIAL DEBUGGING TOOL' for tables, listing specific failure scenarios and what it reveals (dimensions, cell positions, content). It distinguishes itself from sibling tools like 'populate_existing_table' by focusing on debugging and inspection.

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 provides explicit when-to-use scenarios (e.g., 'table population put data in wrong cells', 'table not found errors') and workflow integration steps. It does not explicitly state when not to use, but the guidance is clear and context-rich.

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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