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emailwarmup

set_warm_style

Teach warm-up mail how you write: paste up to 4,000 characters of your own sent emails (tone, length, sign-offs). Empty clears it. Used when the AI writes your warm-up conversations.

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": {
      "warm_style": "<warm_style>"
    },
    "name": "set_warm_style"
  }
}

Input Schema

NameRequiredDescriptionDefault
warm_styleYesWriting samples, max 4,000 characters; empty clears

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior3/5

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

With readOnlyHint=false and destructiveHint=false already supplied, the description's useful additions are the clearing semantics and the context that this feeds AI-written warm-up conversations. It does not disclose that idempotentHint=false means re-calling overwrites rather than appends, nor whether the style applies per mailbox or account-wide.

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?

Three short sentences, front-loaded with the action, then the limit, then the clearing rule and context. No filler or repetition.

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?

For a single-parameter tool with annotations and full schema coverage, the definition covers purpose, limit, clearing, and use case. Remaining gaps are scope (per-mailbox vs. account) and overwrite behavior, which are minor but real.

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% and already documents the 4,000-character max and empty-clears behavior, so the baseline is 3. The description adds marginal value by clarifying what kind of content to paste (tone, length, sign-offs).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action (teach the warm-up engine your writing style) with a concrete resource (your own sent emails) and constraints (4,000 chars, tone/length/sign-offs). It is clearly distinct from siblings like warmup_stats or set_pool_mode, though it never names them explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a context cue ('Used when the AI writes your warm-up conversations') and a clearing behavior ('Empty clears it'), so the agent knows the trigger. It does not state when not to use it or point to any alternative tool for managing warm-up behavior.

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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