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josedu90

MCP Google Workspace Server

docs_update_content

Update the content of a Google Doc by specifying the document ID and providing the new content. Designed for use with the MCP Google Workspace Server to enhance document management through AI-driven interactions.

Instructions

Update the content of a Google Doc

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesNew content
document_idYesID of the document

Implementation Reference

  • MCP tool handler that extracts arguments (document_id, content) and calls DocsService.update_document_content
    async def _handle_docs_update_content(
        self, context: GoogleWorkspaceContext, arguments: dict
    ) -> Dict[str, Any]:
        """Handle docs update content requests."""
        document_id = arguments.get("document_id")
        content = arguments.get("content")
    
        if not document_id or content is None:
            raise ValueError("Both document_id and content are required")
    
        logger.debug(f"Updating document - ID: {document_id}, Content length: {len(content)}")
        result = await context.docs.update_document_content(
            document_id=document_id, content=content
        )
        logger.debug("Document content updated successfully")
        return result
  • Input schema definition for the docs_update_content tool, specifying document_id and content as required string parameters
    types.Tool(
        name="docs_update_content",
        description="Update the content of a Google Doc",
        inputSchema={
            "type": "object",
            "properties": {
                "document_id": {"type": "string", "description": "ID of the document"},
                "content": {"type": "string", "description": "New content"},
            },
            "required": ["document_id", "content"],
        },
    ),
  • Helper method in DocsService that performs the actual Google Docs API batchUpdate to insert new content at index 1 (effectively replacing content)
    async def update_document_content(self, document_id: str, content: str) -> Dict[str, Any]:
        """Update the content of a Google Doc."""
        try:
            service = await self.get_service()
            requests = [{"insertText": {"location": {"index": 1}, "text": content}}]
    
            result = await asyncio.to_thread(
                service.documents()
                .batchUpdate(documentId=document_id, body={"requests": requests})
                .execute
            )
    
            return {"success": True, "result": result}
        except HttpError as error:
            return {"success": False, **self.handle_error(error)}
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is an update operation, implying mutation, but doesn't cover critical aspects like required permissions, whether changes are reversible, rate limits, or what happens to existing content not mentioned. This leaves significant gaps for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words, making it highly concise and front-loaded. Every part of the sentence directly contributes to understanding the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral traits (e.g., permissions, side effects), usage context, and output expectations, leaving the agent with incomplete information to operate effectively.

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?

Schema description coverage is 100%, so the schema already documents both parameters ('document_id' and 'content') adequately. The description adds no additional meaning beyond implying that 'content' replaces existing content, which is minimal value over what the schema provides, justifying the baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Update') and resource ('content of a Google Doc'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'docs_create' or 'sheets_update_values' beyond mentioning Google Docs specifically.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing an existing document), exclusions, or comparisons to siblings like 'docs_get_content' for read operations or 'sheets_update_values' for similar updates in Sheets.

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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