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Tailor resume

tailor_resume

Analyze a resume against a job description to produce a fit score, keyword gaps, honest bullet rewrites, and a cover note, without fabricating experience.

Instructions

Tailor a resume to a specific job description without fabricating experience. Returns a fit score, matched/missing keywords, truthful rewrites of existing bullets, honest gaps, and a short cover note.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resumeYesThe candidate's full resume text (Markdown or plain text).
job_descriptionYesThe full text of the job posting to tailor toward.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
verdictYesOne sentence: is this worth applying to and why.
cover_noteYesA 3-4 sentence outreach note referencing the specific role.
honest_gapsYesReal gaps the candidate should be ready to address in a screen.
match_scoreYesHonest 0-100 fit estimate.
matched_keywordsYesKeywords from the posting the resume already supports.
missing_keywordsYesKeywords the posting wants that the resume does NOT support.
tailored_bulletsYesRewrites of EXISTING bullets only — never fabricated.
Behavior4/5

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

No annotations exist, so the description bears full burden. It explicitly states key behavioral traits: 'without fabricating experience', 'truthful rewrites', 'honest gaps'. This assures the agent of ethical, non-destructive behavior. Could mention that it does not modify the original resume, but the return format implies no side effects.

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?

Single sentence efficiently encapsulates purpose, ethical stance, and output list. Every phrase earns its place; no redundant words. Front-loaded with main action and constraint.

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

Completeness5/5

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

Given 2 parameters with full schema coverage, an output schema (so return values already documented), and moderate complexity, the description sufficiently covers the tool's role and behavior. It leaves no critical gaps for selection and invocation.

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%, so baseline is 3. The description does not add extra meaning beyond the schema for `resume` and `job_description` parameters, but it contextualizes their role in the tailoring process. No additional format or constraint details are needed.

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?

Description uses specific verb 'Tailor' and resource 'resume to a specific job description', clearly stating the output components (fit score, matched/missing keywords, rewrites, gaps, cover note). It distinguishes from siblings like extract_keywords and score_fit by combining tailoring, scoring, and rewriting.

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

Usage Guidelines3/5

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

The description implies usage when a resume and job description are available and one wants to tailor without fabrication, but it does not explicitly state when to use this tool over siblings like extract_keywords or score_fit, nor provide when-not or prerequisite conditions.

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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