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aaronsb

Google Workspace MCP Server

by aaronsb

bulk_operations

Run multiple Google Workspace operations in one call—sequentially chaining results with $N references, or batch a single operation across many resources to reduce round trips.

Instructions

Do many things in one call, two ways. mode:'queue' (default) runs different operations in sequence, chaining results with $N.field — works for every tool. mode:'batch' does ONE operation across many resources in a single Google request — far fewer round trips, but only where Google publishes a batch method.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoqueue (default): N operations, N API calls, in order, with $N.field references between them. batch: ONE operation across many resources in a single API call — no $N references, because there is no 'between'.
toolNobatch only — the tool to call, e.g. 'manage_contacts'.
emailNobatch only — the account to act as.
itemsNobatch only — one entry per resource. A bare id string is enough when that is all that differs, e.g. ['people/c1', 'people/c2']; use objects when items carry more, e.g. [{name: 'Ada Lovelace', contactEmail: 'ada@example.com'}]. Anything shared by the WHOLE batch — labels to add, a field mask — goes at the top level of this call, not in here.
detailNosummary: one-line status per operation (default) | full: include complete output from each operation
operationNobatch only — the operation to apply to every item, e.g. 'delete'. Ask for one that cannot batch and the error names the ones that can.
operationsNoOperations to execute sequentially
Install Server

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It explains that queue mode executes operations sequentially, supports $N.field chaining, and that batch mode reduces round trips but only where Google publishes a batch method. This is meaningful behavioral context, though it does not discuss side effects, error behavior beyond the schema's onError field, or return shape.

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 two sentences, front-loads the core purpose, and packs the essential mode distinction into a compact, readable form. Every clause earns its place, and the queue/batch contrast is immediately clear.

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?

For a complex 7-parameter tool with no output schema and no annotations, the description plus a 100%-covered input schema is nearly complete. It explains the two modes, sequencing, chaining, and the batch limitation. It does not warn about destructive side effects or enumerate which Google operations have batch support, but the schema's operation description points to the error message for that.

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 coverage is 100%, so the baseline is 3. The description reinforces the mode distinction and the 'one call' semantics, but it does not add parameter-level information beyond what the input schema already provides for tool, email, items, detail, operation, and operations.

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's function: perform many operations in a single call, with two distinct modes. It differentiates the internal modes (queue vs batch) but does not explicitly distinguish itself from its sibling queue_operations, so it stops just short of full sibling differentiation.

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 gives practical guidance: queue is the default and works for every tool, while batch is appropriate when one operation applies across many resources and Google exposes a batch method. It clearly contrasts the two modes, though it does not explicitly state when to prefer individual sibling tools over this bulk tool.

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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