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Kav-png

quantjobs

by Kav-png

cv_draft_update

Apply structural edits to a CV draft: update summary, reorder sections, and modify skills. Enables tailored changes to profile, experience, and more.

Instructions

Apply a structural edit to a CV draft (summary, ordering, skills) and return content.

Use this for the summary, reordering, and skills. For rewording individual experience bullets, prefer cv_reframe_bullet (it logs the change). Patch keys: profile (str summary), contact (dict), experience ([{org, role?, location?, start?, end?, bullets?}] merged by org), skills_rows ([{label, value}] merged by label), education/awards/volunteering (replace the list). If you reword bullets here, also call cv_note_add for anything beyond a literal restatement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
patchYes
draft_idYes
Behavior4/5

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

Without annotations, the description carries full burden. It explains the tool applies a structural edit, returns content, and details merging behavior for certain patch keys. It also notes that cv_draft_update does not log changes, implicitly distinguishing it from cv_reframe_bullet. However, it could be more explicit about whether the tool saves or versions the draft.

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 paragraph with purposeful sentences. It starts with the main purpose, followed by usage guidance, then detailed patch key documentation. Every sentence adds value; no wasted words.

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 complexity (2 params, one nested object), no annotations, and no output schema, the description provides sufficient context for an AI agent. It explains the tool's functionality, usage boundaries, and patch key structure. However, it omits details about the return value format, which could be useful.

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

Parameters5/5

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

While the input schema has minimal description (0% coverage), the description fully compensates by listing all valid patch keys with their types and examples (e.g., profile, contact, experience, skills_rows, education/awards/volunteering). This adds crucial meaning that the generic schema lacks.

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 applies a structural edit to a CV draft (summary, ordering, skills) and returns content. It also explicitly distinguishes from the sibling cv_reframe_bullet for rewording bullets.

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

Usage Guidelines5/5

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

Provides explicit guidance on when to use this tool ('Use this for the summary, reordering, and skills') and when to prefer an alternative ('For rewording individual experience bullets, prefer cv_reframe_bullet'). Also includes additional instruction to call cv_note_add for non-literal rewording.

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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