google-workspace-mcp-advanced

update_script_content

Update Apps Script project files by sending a JSON array of file objects. Use dry run to preview changes before applying.

Instructions

Update Apps Script project files.

Args: user_google_email: The user's Google email address. Required. script_id: The Apps Script project ID. files_json: JSON list of file objects (name, type, source). dry_run: Preview content update without mutation. Defaults to True.

Returns: str: Confirmation of preview or update.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "user_google_email": "<user_google_email>",
      "script_id": "<script_id>",
      "files_json": "<files_json>"
    },
    "name": "update_script_content"
  }
}

Input Schema

Name	Required	Description	Default
`user_google_email`	Yes
`script_id`	Yes
`files_json`	Yes
`dry_run`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.10Jun 2, 2026

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations, the description carries full burden for behavioral disclosure. It covers the dry_run parameter's purpose and default, and the return type, but does not explain whether the update is incremental or full replacement, nor error conditions or side effects like overwriting existing files.

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 concise and well-structured with Args and Returns sections. Every sentence serves a purpose without redundancy. The key information is front-loaded.

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

Completeness3/5

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

Given 4 parameters, no annotations, and a simple output schema, the description covers parameters and return value but omits important details like whether the update replaces all files or merges, and the exact nature of the 'preview' response. This leaves gaps for an agent to fully understand the tool's behavior.

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?

Since schema description coverage is 0%, the description adds critical meaning: it labels user_google_email as required, specifies files_json structure, and explains dry_run's default and behavior. However, it could provide more detail on file object fields (e.g., allowed types) and script_id context.

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 tool updates Apps Script project files, with specific verb and resource. It distinguishes from siblings like 'get_script_content' or 'create_script_project' by naming the action and key parameters, though it does not explicitly differentiate from similar update tools like 'update_deployment'.

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?

The description provides no guidance on when to use this tool versus alternatives, nor any conditions for usage. It describes what the tool does but does not set expectations for appropriate contexts or exclusions.

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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