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Design email with AI

design_email_template
Read-onlyIdempotent

Use AI to generate a complete email HTML template based on a description. Returns ready-to-use HTML that can be sent or used in campaigns.

Input Schema

NameRequiredDescriptionDefault
typeYesType of email template to generate
styleYesVisual style/aesthetic for the template
apiKeyNoSend16 API key (overrides SEND16_API_KEY env var)
brandColorNoPrimary brand color as hex code (e.g. "#4F46E5")
companyNameNoCompany or brand name to include in the template
descriptionYesDetailed description of the email content and purpose (e.g. 'Welcome email for a SaaS product with feature highlights and CTA to start trial')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds that the result is ready-to-use HTML suitable for sending or campaigns, but does not disclose whether the template is persisted (or requires a follow-up save_template call), nor any latency/cost of AI generation.

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 tight sentences with the generation action front-loaded and the output payoff second; no filler. It could be marginally tighter but nothing is wasted.

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

Completeness3/5

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

There is no output schema, so the description must carry the return-value burden, and it does only in one clause ('ready-to-use HTML'). For a six-parameter AI generation tool, the workflow gap (does it save? what does the HTML payload look like? does it need an API key configured?) is left open.

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% with two enum-constrained parameters, so the schema already carries the parameter meaning. The description adds nothing about type/style/description limits or how brandColor and companyName influence the output, so baseline 3 applies.

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?

Specific verb (generate) plus resource (complete email HTML template) and the input basis (a description). It is distinguishable from save_template and list_templates, but it never distinguishes itself from the close siblings generate_email_from_url or render_email_content, which would require explicit contrast.

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?

No when-to-use, when-not-to-use, or alternative-tool guidance. The agent must infer that generate_email_from_url is the URL-based counterpart and that this tool produces an unsaved artifact, neither of which the description states.

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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