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google-services-mcp

by nabheet

Batch update document

google_docs_batch_update

Send raw Google Docs batchUpdate requests to modify document styles, tables, headers, and other elements in a single API call.

Instructions

Send raw Docs batchUpdate requests (styles, tables, headers, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNoAccount nickname to use.
requestsYesDocs API batchUpdate requests.
documentIdYesDocument ID.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.30
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / requests / items / propertyNames
      Added value: +{
      +  "type": "string"
      +}
  2. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations are absent, so the description carries the full burden, but it only restates the operation and offers examples of request types. It does not disclose the operation's mutation effect on the document, what the API response contains, error behavior, or any per-call limits. This goes little beyond what the tool name already implies.

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?

Single sentence with no filler; the verb and resource are front-loaded and the parenthetical adds useful scope. It is appropriately minimal for what it conveys, though it could afford a second sentence on usage without harming clarity.

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?

With no annotations and no output schema, the description is thin for a low-level API passthrough. It omits return value shape, side effects, and how errors manifest, and it does not connect to sibling higher-level update tools. The schema's parameter coverage is complete, but the overall tool contract remains under-specified.

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 covers all three parameters with simple descriptions, so baseline is 3. The description adds modest value by giving examples of request kinds (styles, tables, headers) that clarify the otherwise open-ended 'requests' array, but it does not explain the request object structure or required fields, leaving most semantics to the Docs API knowledge.

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?

Names a specific action ('Send raw Docs batchUpdate requests') and a resource (the document), with examples of what kinds of updates are supported (styles, tables, headers). It distinguishes from sibling read/create/text-insert tools because it targets the low-level batchUpdate endpoint, though 'batchUpdate' in the title partially duplicates that idea.

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 on when to choose raw batchUpdate over the higher-level google_docs_insert_text, google_docs_replace_text, or google_docs_create, nor when not to use it. The word 'raw' implies advanced use, but this is not an explicit recommendation or exclusion, so an agent must infer timing.

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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