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Kav-png

quantjobs

by Kav-png

cv_draft_create

Generate a conversational CV draft, either base or tailored to a specific job, returning editable content and skill gap analysis.

Instructions

Start a conversational CV draft — base (job_id=0) or tailored to a job.

Returns {draft_id, content, missing_skills, suggested_projects}. content is the editable CV (contact, profile summary, education, experience-with-selected-bullets, skills_rows, volunteering, awards). Show it to the user, then refine it with cv_draft_update / cv_reframe_bullet and render with cv_draft_render.

POLICY: bullets may be reframed and strengthened (the user wants punchy bullets). Any change that goes beyond a literal restatement MUST be logged via cv_reframe_bullet (preferred — it edits and logs atomically) or cv_note_add, classified as reframe | stretch | fabrication, so it lands in cv_notes.md. Never silently invent. Missing skills are returned (and logged as pending projects).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idNo
stretch_levelNo
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses behavioral traits: bullets may be reframed and strengthened, any change beyond literal restatement must be logged, missing skills are returned and logged. This is reasonably transparent for a draft creation tool.

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

Conciseness3/5

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

The description is moderately concise, with a clear first sentence and structured blocks for returns and policy. It could be slightly tighter but is not overly verbose.

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 tool has only 2 parameters, no output schema, and the description covers workflow, return structure, and policy, it is largely complete for its complexity. The missing explanation of stretch_level is the main gap.

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?

The schema coverage is 0%, meaning the description should fully explain parameters. It partially explains job_id ('base (job_id=0) or tailored to a job') but does not explain stretch_level at all. This leaves a significant gap in understanding.

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 starts a conversational CV draft, either base or tailored to a job, and lists return fields. It distinguishes from siblings like cv_draft_update and cv_draft_render by mentioning them as subsequent steps.

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

Usage Guidelines4/5

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

The description explains when to use the tool (to start a draft) and provides a policy for subsequent actions (refine with cv_draft_update, log changes). However, it does not explicitly state when not to use it or contrast with alternatives.

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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