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tom275275

Google Workspace MCP Server

by tom275275

get_gmail_threads_content_batch

Fetch content from up to 25 Gmail threads in one batch request, grouping thread IDs to prevent SSL connection exhaustion.

Instructions

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

Args: thread_ids (List[str]): A list of Gmail thread IDs to retrieve. The function will automatically batch requests in chunks of 25. user_google_email (str): The user's Google email address. Required.

Returns: str: A formatted list of thread contents with separators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
thread_idsYes
user_google_emailYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.14.3

TDQS

A4.6/5.0
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 transparently explains that the function automatically chunks requests in groups of 25, mentions the maximum batch size, and states the return format as a formatted string with separators. This goes beyond a bare 'retrieves threads' statement, though it does not cover error or authorization behavior.

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 compact and front-loaded: purpose, batch limit, and rationale appear first. The Args and Returns sections are concise and each sentence adds useful information with no filler or repetition.

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?

For a two-parameter tool with no annotations, the description is complete enough to call correctly: it covers what the tool does, how batching behaves, what input is required, and what output shape to expect. The presence of an output schema further reduces the need to describe return values in greater detail.

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?

Schema description coverage is 0%, so the description must compensate, and it does. It explains thread_ids are Gmail thread IDs and that batching is automatic, and it notes user_google_email is the user's Google email address and is required. Both parameters receive meaningful semantic context beyond the raw schema.

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 opens with a specific verb and resource: 'Retrieves the content of multiple Gmail threads in a single batch request.' This clearly distinguishes the tool from the singular sibling get_gmail_thread_content and the message-level batch tool get_gmail_messages_content_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?

It states the intended use case: retrieving multiple threads efficiently in one batch, and adds a concrete rationale for the 25-thread limit: preventing SSL connection exhaustion. It does not explicitly name the singular alternative get_gmail_thread_content, but the batch framing makes the appropriate context clear.

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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