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AIPOST-EMAIL

AIPost.email MCP Server

Official
by AIPOST-EMAIL

reply_to

Reply to existing email messages by automatically fetching the original to set correct recipient, thread ID, and in-reply-to headers. Use recipient fallback when replying to sent messages.

Instructions

Reply to an existing message. Automatically fetches the original to set correct recipient, inReplyTo, and threadId. Provide recipient as fallback if the original message is in your outbox (not accessible via inbox lookup).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyMdNoOptional Markdown body
payloadYesStructured payload for the reply
subjectNoReply subject (defaults to Re: original subject)
priorityNo
taskTypeYesTask type for the reply
messageIdYesID of the message to reply to
recipientNoFallback recipient if the original message cannot be found in the inbox (e.g., when replying to a message you sent).
ttlSecondsNoTime-to-live in seconds. Set to -1 for messages that never expire.
signMessageNoAdd ED25519 signature to the reply
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the auto-fetch behavior and fallback recipient handling, which is valuable. However, it does not mention error handling, authentication needs, or whether the reply is sent immediately, leaving gaps for a mutation tool.

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?

Two sentences, front-loaded with the action, and no redundant information. Every sentence earns its place, explaining both the primary behavior and an edge case.

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

Completeness2/5

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

For a tool with 9 parameters, a nested payload, and no output schema, the description is incomplete. It explains the core flow but does not help with constructing the payload, understanding taskType values, or handling other parameters like ttlSeconds or signMessage. The high schema coverage partially compensates, but the vague payload and lack of output schema leave significant gaps.

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 coverage is 89%, so most parameters have descriptions. The description adds context by explaining that messageId triggers an auto-fetch that sets inReplyTo and threadId, and that recipient is used as a fallback. However, the payload parameter remains vague, and the description does not elaborate beyond the 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 clearly states the action ('Reply to an existing message') with a specific verb and resource. It also explains the automatic behavior of fetching the original to set recipient, inReplyTo, and threadId, distinguishing it from sibling tools like send_message.

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?

Clearly indicates when to use the tool (when replying to an existing message) and provides a specific fallback condition (when the original is in the outbox). It does not explicitly name alternative tools, but the response makes the use case clear. Lacks explicit exclusions.

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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