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aaddyy_email_writer

Generate professional, context-aware emails for business, sales, support, or personal use by setting tone, style, length, and recipient details.

Instructions

Generate professional, personalized, and context-aware emails for any purpose using AI. Supports multiple tones, styles, and customization options. (~3 credits)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ctaNo
goalNo
toneNoTone of the emailformal
topicYesTopic or subject of the email
lengthNoLength of the emailmedium
keywordsNo
languageNo
emailTypeYesType of email to generate
signatureNo
versionCountNoNumber of email variations to generate
writingStyleNo
previewFormatNo
recipientNameNo
recipientRoleNo
generateSubjectLineNo
personalizationTagsNo
recipientRelationshipNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the cost (~3 credits), which is genuinely useful, but says nothing about output format, whether variations are returned, permission or account requirements, or what happens when optional personalization inputs are omitted.

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

Conciseness4/5

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

Two compact sentences with the core capability front-loaded and the cost appended efficiently. Nothing is wasted, though the second sentence is generic filler that could have carried real parameter or routing information.

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 17-parameter generative tool with no annotations, no output schema, and very low schema coverage, the description is far too thin. An agent cannot learn required inputs beyond the schema, expected output shape, or how variations and length/tone interact.

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

Parameters2/5

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

Schema coverage is only 29% across 17 parameters, so the schema leaves most inputs (cta, goal, keywords, signature, recipient fields, personalizationTags, etc.) undocumented. The phrase 'tones, styles, and customization options' vaguely gestures at the tone/writingStyle parameters but clarifies nothing about the remaining 14 parameters.

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 verb (generate) and resource (emails) with a clear quality claim and scope ('any purpose'). However it does not differentiate from the close sibling aaddyy_job_email_creator, which generates a closely related artifact, leaving the agent to guess which generator to call.

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

Usage Guidelines2/5

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

The description offers no when-to-use guidance, no prerequisites, and no routing to alternatives. Given that job_email_creator and cover_letter_creator exist as siblings, an explicit boundary would have been valuable.

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