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Google Workspace MCP Server

draft_gmail_message

Create Gmail draft emails, including replies, with support for CC, BCC, HTML, and threading. Specify recipient, subject, and body to draft new messages or reply within existing threads.

Instructions

Creates a draft email in the user's Gmail account. Supports both new drafts and reply drafts.

Args: user_google_email (str): The user's Google email address. Required. subject (str): Email subject. body (str): Email body (plain text). body_format (Literal['plain', 'html']): Email body format. Defaults to 'plain'. to (Optional[str]): Optional recipient email address. Can be left empty for drafts. cc (Optional[str]): Optional CC email address. bcc (Optional[str]): Optional BCC email address. thread_id (Optional[str]): Optional Gmail thread ID to reply within. When provided, creates a reply draft. in_reply_to (Optional[str]): Optional Message-ID of the message being replied to. Used for proper threading. references (Optional[str]): Optional chain of Message-IDs for proper threading. Should include all previous Message-IDs.

Returns: str: Confirmation message with the created draft's ID.

Examples: # Create a new draft draft_gmail_message(subject="Hello", body="Hi there!", to="user@example.com")

# Create a plaintext draft with CC and BCC
draft_gmail_message(
    subject="Project Update",
    body="Here's the latest update...",
    to="user@example.com",
    cc="manager@example.com",
    bcc="archive@example.com"
)

# Create a HTML draft with CC and BCC
draft_gmail_message(
    subject="Project Update",
    body="<strong>Hi there!</strong>",
    body_format="html",
    to="user@example.com",
    cc="manager@example.com",
    bcc="archive@example.com"
)

# Create a reply draft in plaintext
draft_gmail_message(
    subject="Re: Meeting tomorrow",
    body="Thanks for the update!",
    to="user@example.com",
    thread_id="thread_123",
    in_reply_to="<message123@gmail.com>",
    references="<original@gmail.com> <message123@gmail.com>"
)

# Create a reply draft in HTML
draft_gmail_message(
    subject="Re: Meeting tomorrow",
    body="<strong>Thanks for the update!</strong>",
    body_format="html,
    to="user@example.com",
    thread_id="thread_123",
    in_reply_to="<message123@gmail.com>",
    references="<original@gmail.com> <message123@gmail.com>"
)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ccNoOptional CC email address.
toNoOptional recipient email address.
bccNoOptional BCC email address.
bodyYesEmail body (plain text).
subjectYesEmail subject.
thread_idNoOptional Gmail thread ID to reply within.
referencesNoOptional chain of Message-IDs for proper threading.
body_formatNoEmail body format. Use 'plain' for plaintext or 'html' for HTML content.plain
in_reply_toNoOptional Message-ID of the message being replied to.
user_google_emailYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses key behaviors: creates rather than sends, supports reply drafts via thread_id/in_reply_to/references, and returns the draft ID. It also explains the effect of optional parameters like 'to' being left empty. However, it does not mention authentication requirements or potential side effects, which would be useful.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the purpose and includes useful examples, but the Args section largely repeats the schema descriptions (90% coverage) and adds length. There is also a typo in the last example (missing quote). Overall it is well-organized but not maximally concise.

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?

Given the tool has 10 parameters and no annotations, the description covers all parameters and provides examples for new drafts, reply drafts, HTML/plain text, and optional CC/BCC. It also states the return value. This is quite complete, though it omits error cases and authentication context.

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

Parameters4/5

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

Schema coverage is 90%, so baseline is 3. The description adds meaningful nuance beyond the schema: thread_id 'creates a reply draft', references 'should include all previous Message-IDs', and body_format default. The Args section partly duplicates schema descriptions but the extra context justifies a 4.

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 'Creates a draft email in the user's Gmail account' with a specific verb and resource. It also distinguishes from send_gmail_message by explicitly supporting both new and reply drafts, making the purpose unambiguous.

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 clear context on when to use the tool (creating drafts, including reply drafts with threading parameters). It does not explicitly name alternatives or exclusions, but the sibling tool send_gmail_message is implicitly contrasted by the draft nature. This is clear but not fully explicit.

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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