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bkbaheti

MCP Google Multi-Account Server

by bkbaheti

gmail_batch_modify_labels

Batch apply or remove Gmail labels to multiple messages at once, handling up to 1000 per operation to save time and streamline organization.

Instructions

Apply label changes to multiple messages in one operation. More efficient than individual modifications. Limited to 1000 messages. Requires full scope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confirmNoSet to true to confirm bulk operation (required for >100 messages)
accountIdYesThe Google account ID, alias, or email
messageIdsYesArray of message IDs to modify (max 1000)
addLabelIdsNoLabel IDs to add to all messages
removeLabelIdsNoLabel IDs to remove from all messages
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing behavioral traits. It does add valuable context: 'Limited to 1000 messages' and 'Requires full scope' inform about constraints and permissions. However, it does not disclose potential side effects, partial failure behavior, or the distinction between add and remove label operations beyond the schema. This is decent but not comprehensive.

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 three concise sentences, each earning its place: purpose, efficiency argument, and key constraints (limit and scope). It is front-loaded with the primary action and contains no fluff or repetition.

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 the tool has 5 parameters, no output schema, and no annotations, the description provides moderate context. It covers purpose, efficiency, limits, and scope, but lacks explicit guidance on batch confirmation, error handling, or alternatives. For a bulk mutation tool, more detail would enhance completeness, but the description is not grossly inadequate.

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 each parameter already has clear descriptions. The description itself does not add additional semantic detail beyond the schema, except for reinforcing the 1000-message limit, which is also present in the messageIds parameter description. Baseline score of 3 is appropriate.

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 function: 'Apply label changes to multiple messages in one operation.' This is a specific verb+resource combination that distinguishes it from sibling tools like gmail_modify_labels, which handles single messages. The scope is also clarified with 'multiple messages' and the 1000-message limit.

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 context for when to use this tool: 'More efficient than individual modifications' implies a bulk operation use case. It also notes the 1000-message limit and full scope requirement, which are important prerequisites. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of full guidance.

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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