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redesignhealth

Google Workspace MCP Server

get_gmail_messages_content_batch

Batch fetch Gmail message contents for up to 25 message IDs at once, returning subject, sender, date, recipients, and body. Prevents SSL connection exhaustion during bulk retrieval.

Instructions

Retrieves the content of multiple Gmail messages in a single batch request. Supports up to 25 messages per batch to prevent SSL connection exhaustion.

Args: message_ids (List[str]): List of Gmail message IDs to retrieve (max 25 per batch). user_google_email (str): The user's Google email address. Required. format (Literal["full", "metadata"]): Message format. "full" includes body, "metadata" only headers.

Returns: str: A formatted list of message contents including subject, sender, date, Message-ID, recipients (To, Cc), and body (if full format).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNofull
message_idsYes
user_google_emailYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Since no annotations are provided, the description carries the transparency burden. It discloses the batch behavior, the message limit, and the effect of the 'format' parameter on output. However, it does not explicitly state that the operation is read-only or describe authentication requirements, which would be useful but are not critical given the tool's obvious read nature.

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 well-structured with a clear summary, Args section, and Returns section. Every sentence is informative and there is no redundant text. It is appropriately sized for the tool's functionality.

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

Completeness5/5

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

The description is complete for the tool's complexity: it covers parameters, limits, and the return format. It explains what the returned string contains, which is especially important given that no output schema is shown. Sibling tools are not necessary for this description to be self-sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description thoroughly explains each parameter beyond the schema: message_ids is a list of Gmail message IDs with a maximum of 25, user_google_email is required and specifies whose email, and format describes the difference between 'full' and 'metadata'. This adds significant semantic value, compensating for the 0% schema coverage.

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 purpose: 'Retrieves the content of multiple Gmail messages in a single batch request.' It specifies the resource (Gmail messages) and distinguishes from sibling tools like get_gmail_message_content by explicitly indicating 'multiple' and 'batch'.

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 implies when to use this tool: for retrieving multiple messages in a batch, with a cap of 25 to prevent SSL connection exhaustion. It does not explicitly name alternatives or exclude use cases, but the context is clear enough for an agent to choose this instead of the singular variant.

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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