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build_email_template

Build and validate email templates by assembling components; receive a structured preview with personalization details and validation warnings.

Instructions

Build and validate email template, returning structured preview (NO HTML).

Each component is a dict with a "type" key and component-specific parameters. Component types: header, title, text, button, spacer, divider, image, disclaimer, footer, jinja_block.

Returns a rich structured preview with:

  • structure: Ordered list of components with content previews

  • personalization: Jinja variables, user attributes, conditionals

  • summary: Auto-generated human-readable description

  • validation: Errors and warnings

  • component_count, html_bytes

HTML is built internally but NOT returned (prevents context overflow). Use debug=True to save HTML to temp file for inspection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
componentsYes
debugNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It discloses that HTML is built internally but not returned, and that debug=True saves to temp file. It also details component types and return structure, providing good insight into behavior. However, it does not mention any side effects or limitations like file size.

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?

The description is well-structured, starting with purpose, then component types, then return preview details, and debug behavior. Each section adds value. Slightly lengthy but justified given the complexity; could trim redundant phrases.

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?

Given 3 parameters, 2 required, no annotations, and an existing output schema (not shown), the description covers the output structure extensively, including validation and expected fields. It lacks mention of error handling or prerequisites but is fairly complete for the tool's complexity.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It explains component types and the debug parameter's effect well, listing possible component types. However, it does not detail the meaning of the 'title' parameter beyond being required, and the component schema is left to arbitrary additionalProperties.

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's purpose: 'Build and validate email template, returning structured preview (NO HTML).' It uniquely distinguishes from siblings by focusing on building and validating templates, while siblings like compare_templates, localize_template, or patch_template_text have different functions.

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 does not provide explicit when-to-use or when-not-to-use guidance compared to sibling tools. It mentions the debug parameter for HTML inspection but lacks context on when to choose this tool over alternatives like analyze_template or get_personalized_preview.

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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