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bulk_update_google_doc

Batch multiple Google Docs operations like text insertion, formatting, and table creation into a single API call to reduce latency and improve performance for complex document updates.

Instructions

Execute multiple document operations in a single batched API call for improved performance.

This tool allows you to perform many operations at once instead of making separate tool calls. Operations are batched into groups of up to 50 requests (Google Docs API limit) and executed sequentially. This significantly reduces latency when making complex document changes.

Performance: 5-10x faster than individual tool calls for multi-operation workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
document_idYesThe ID of the Google Document to update
operationsYesList of operations to perform. Each operation is a dictionary with a 'type' field and operation-specific parameters. Supported operation types: 1. insert_text: Insert text at a specific index - text: Text to insert (string) - index: Position to insert at (1-based integer) - tab_id: Optional tab ID (string) 2. delete_range: Delete a range of content - start_index: Start of range (1-based, inclusive) - end_index: End of range (1-based, exclusive) - tab_id: Optional tab ID (string) 3. apply_text_style: Apply character-level formatting - Either (start_index, end_index) OR (text_to_find, match_instance) - Style properties: bold, italic, underline, strikethrough, font_size, font_family, foreground_color, background_color, link_url 4. apply_paragraph_style: Apply paragraph-level formatting - Either (start_index, end_index) OR (text_to_find, match_instance) OR index_within_paragraph - Style properties: alignment, indent_start, indent_end, space_above, space_below, named_style_type, keep_with_next 5. insert_table: Insert a table - rows: Number of rows (integer) - columns: Number of columns (integer) - index: Position to insert (1-based integer) 6. insert_page_break: Insert a page break - index: Position to insert (1-based integer) 7. insert_image_from_url: Insert an image from a URL - image_url: URL to the image (string) - index: Position to insert (1-based integer) - width: Optional width in points (float) - height: Optional height in points (float) Example: [ {"type": "insert_text", "text": "# Title\n\n", "index": 1}, {"type": "apply_paragraph_style", "start_index": 1, "end_index": 8, "named_style_type": "HEADING_1"}, {"type": "insert_text", "text": "Introduction text.\n", "index": 8}, {"type": "insert_table", "rows": 3, "columns": 2, "index": 27} ]
tab_idNo

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 full burden and does well. It discloses key behavioral traits: batching up to 50 requests (API limit), sequential execution, performance improvement (5-10x faster), and that it's for document updates. It doesn't mention error handling, atomicity, or authentication requirements, but covers the essential operational characteristics.

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 appropriately sized and front-loaded with the core purpose in the first sentence. Each subsequent sentence adds value: explaining the batching mechanism, performance benefits, and quantitative comparison. There's minimal redundancy, though the performance claim could be slightly more concise.

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?

Given the tool's complexity (batch operations with multiple parameter types) and no annotations, the description provides good context about the batching mechanism and performance benefits. Combined with the detailed input schema (67% coverage) and presence of an output schema, the overall documentation is quite complete. It could benefit from mentioning error handling or atomicity guarantees.

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?

The description doesn't directly discuss parameters, but the input schema has 67% coverage with detailed documentation of the 'operations' parameter including all supported operation types with examples. Since schema coverage is moderate (67%), the comprehensive schema documentation adequately compensates. The description's focus on batch execution context provides valuable semantic framing for the parameters.

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: 'Execute multiple document operations in a single batched API call for improved performance.' It specifies the verb ('execute'), resource ('document operations'), and distinguishes from siblings by emphasizing batch processing versus individual operations. The performance comparison further differentiates it from single-operation tools.

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 clear context for when to use this tool: 'when making complex document changes' and for 'multi-operation workflows' where performance is important. It explicitly states the alternative ('instead of making separate tool calls') but doesn't specify when NOT to use it or name specific sibling alternatives for simple operations.

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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