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Skeptomenos

google-workspace-mcp-advanced

by Skeptomenos

create_table_with_data

Insert a table with structured data into a Google Doc at the correct position. Requires prior inspection of document structure to determine insertion index.

Instructions

Creates a table and populates it with data in one reliable operation.

CRITICAL: YOU MUST CALL inspect_doc_structure FIRST TO GET THE INDEX!

MANDATORY WORKFLOW - DO THESE STEPS IN ORDER:

Step 1: ALWAYS call inspect_doc_structure first Step 2: Use the 'total_length' value from inspect_doc_structure as your index Step 3: Format data as 2D list: [["col1", "col2"], ["row1col1", "row1col2"]] Step 4: Call this function with the correct index and data

EXAMPLE DATA FORMAT: table_data = [ ["Header1", "Header2", "Header3"], # Row 0 - headers ["Data1", "Data2", "Data3"], # Row 1 - first data row ["Data4", "Data5", "Data6"] # Row 2 - second data row ]

CRITICAL INDEX REQUIREMENTS:

  • NEVER use index values like 1, 2, 10 without calling inspect_doc_structure first

  • ALWAYS get index from inspect_doc_structure 'total_length' field

  • Index must be a valid insertion point in the document

DATA FORMAT REQUIREMENTS:

  • Must be 2D list of strings only

  • Each inner list = one table row

  • All rows MUST have same number of columns

  • Use empty strings "" for empty cells, never None

  • Use debug_table_structure after creation to verify results

Args: user_google_email: User's Google email address document_id: ID of the document to update table_data: 2D list of strings - EXACT format: [["col1", "col2"], ["row1col1", "row1col2"]] index: Document position (MANDATORY: get from inspect_doc_structure 'total_length') bold_headers: Whether to make first row bold (default: true) dry_run: When True (default), return planned mutation without executing it

Returns: str: Confirmation with table details and link

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_google_emailYes
document_idYes
table_dataYes
indexYes
bold_headersNo
dry_runNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. It details mandatory workflow, data format requirements, dry_run default behavior, and return value. Could mention potential failure modes or side effects, but otherwise thorough.

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?

Description is long but well-structured with steps, examples, and emphasis. Every sentence adds value; could be slightly more concise but readability is high.

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 0% schema coverage, complex parameters (2D list, index dependency), and no annotations, the description is exceptionally complete. It includes mandatory pre-step, data validation rules, and verification step. With output schema present, return description is sufficient.

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 description explains each parameter thoroughly: table_data format with example, index source from inspect_doc_structure, bold_headers and dry_run defaults. Only user_google_email and document_id are not elaborated, but they are self-explanatory.

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 creates a table and populates it with data, distinguishing it from siblings like create_sheet or insert_doc_elements by combining creation and population in one operation.

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?

Provides explicit step-by-step workflow, mandates calling inspect_doc_structure first, and warns against using arbitrary indices. Lacks explicit mention of when not to use this tool versus alternatives like insert_doc_elements.

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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