Fastmail MCP Server

suggest_reply

Draft a reply to an email based on your intent, using the LLM to generate a response that suits your instructions.

Instructions

Use the connected LLM to draft a reply to an email based on the provided intent or instructions. Requires the MCP client to support sampling.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "emailId": "<emailId>",
      "intent": "<intent>"
    },
    "name": "suggest_reply"
  }
}

Input Schema

Name	Required	Description	Default
`intent`	Yes	Describe what the reply should convey, e.g. 'Accept the meeting invitation' or 'Politely decline and suggest next week instead'
`emailId`	Yes	The email ID to reply to (use search_emails or get_latest_emails to find IDs)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.3.0Aug 2, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description adds useful context by stating it uses the connected LLM and requires sampling support. But it does not disclose the return format or whether the draft is actually sent/stored, which would be important for an agent to fully understand side effects.

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 concise, information-dense sentences with no redundancy. Each sentence serves a purpose: describing the action and stating a critical requirement.

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?

The description covers the tool's purpose and a key prerequisite, but lacks an explicit statement about the return value or that no side effects occur (e.g., it does not send or save the reply). Without an output schema, this would be helpful, though the simple nature of the tool makes it moderately complete.

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?

The input schema already provides detailed descriptions for both parameters (100% coverage), so the description adds little beyond the schema. Baseline 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 clearly states the tool drafts a reply to an email using the connected LLM, with specific verb ('draft') and resource ('reply to an email'). This distinguishes it from siblings like reply_email (sending) and create_draft (generic draft creation).

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 conveys clear context: use this tool when you need an LLM-generated reply based on intent, and it notes a prerequisite (MCP client sampling support). However, it does not explicitly mention alternatives or when not to use it, leaving some ambiguity against reply_email or create_draft.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.