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laddro.coverLetters.generate

Create a personalized cover letter PDF by providing a job description and specifying the target position. Uses a resume to tailor the letter to the application.

Instructions

AI-generate a personalized cover letter based on a resume and job description. Returns a PDF.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resumeIdNoResume UUID to base the cover letter on (uses default if omitted)
positionNameYesJob title or position name being applied for
jobDescriptionNoFull job description text
jobUrlNoURL to the job posting (alternative to jobDescription)
languageNoOutput language code (e.g. en, de, fr)
templateIdNoTemplate identifier for PDF output
colorIdNoColor scheme identifier
fontNoFont family name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentNoBase64-encoded PDF data
mimeTypeNo
Behavior3/5

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

Annotations already indicate this is not read-only. The description adds that it returns a PDF but does not disclose whether it creates a persistent resource, requires specific permissions, or has any side effects like saving the generated letter.

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?

The description is a single sentence that contains all essential information (action, input, output) without fluff. It is optimally concise.

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 the presence of an output schema and extensive parameter descriptions, the description adequately summarizes the tool. However, it could mention that the output is a PDF stream or a file reference, especially since the output schema likely defines the return structure.

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 coverage is 100%, so each parameter is well-described in the schema. The description adds no additional semantic meaning beyond what the schema provides (e.g., correlation between parameters).

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 explicitly states the action (AI-generate), the input (resume and job description), and the output (PDF). It is a specific verb+resource combination that clearly differentiates from read-only tools like get or list.

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 guidance is provided on when to use this tool over its siblings (laddro.coverLetters.create, render, etc.). The description does not mention alternatives or prerequisites.

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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