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Draft Reply

draft_reply

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ccNoOptional list of additional CC recipients. Each is an object with required "email" and optional "name". Example: [{"email": "bob@acme.com"}]
bccNoOptional list of BCC recipients, same format as "cc".
bodyYesReply body in Markdown, rendered to HTML at send time — same rules as draft_email: hyperlinks as [text](url), single newlines preserved as line breaks.
email_idYesThe database id of the email to reply to (from search_emails results)
reply_allNoIf true, reply to all original recipients (Reply All). Default: false (reply to sender only).
as_teammateNoDraft the reply on a consented teammate's behalf — pass their email. The draft is created in their mailbox; pass the same teammate to send_email to send it. Gated on that teammate's act-on-behalf setting (stricter than conversation sharing); a teammate who hasn't granted it is rejected. Omit to draft from your own account.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations are minimal, so the description carries the burden. It adds meaningful behavioral context beyond annotations: the draft is created but must be confirmed before sending, and it discloses the return shape (success, draft_id, provider, original email context). It does not fully describe side effects like mailbox persistence, but the core behavior is transparent.

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: the action, target, and provider are in the first sentence, followed by the prerequisite workflow and safety rule. The <summary>/<returns> structure is clean and every sentence contributes.

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 no output schema, the description helpfully names the returned fields and gives the essential call sequence. It is complete enough for an agent to invoke correctly, though it could add a bit more about when the draft is actually persisted and what happens if the email_id is invalid.

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 the schema already documents all six parameters in detail, including examples and edge cases. The description adds little parameter-level meaning beyond telling the agent to pass the email id from search_emails, so the baseline 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 opens with a specific verb+resource: "Create a reply draft to an existing email thread (Outlook or Gmail)." It clearly scopes the tool to replying to existing emails, distinguishing it from a general compose flow, and the workflow reference to send_email reinforces its role.

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 gives explicit usage context: use search_emails first, pass the resulting id, and always present the draft for confirmation before send_email. This clearly positions the tool in the reply workflow, though it does not explicitly state when not to use it (e.g., for new emails) or name draft_email as an alternative.

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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