Fact-checked resume tailoring, cover letters, interview prep and keyword gaps for a job posting.
Scored across 7 tools
Most tools target a distinct step (cover letter, interview prep, keyword gap, DOCX/PDF rendering), but tailor_resume and tailor_resume_pdf are the same operation with different outputs, and tailor_resume_pdf also overlaps with keyword_gap and render_resume_pdf by bundling them. The verbose descriptions explain the difference, but an agent wanting just the reworded text could easily pick the PDF variant or vice versa.
All names share the careergenius_ prefix and snake_case, and render_resume_pdf/tailor_resume_pdf follow a clear verb_noun[_format] pattern. However, cover_letter, interview_prep, and keyword_gap are noun-only while the others are verb-led, a minor deviation from a single convention.
Seven tools is a well-scoped surface for a resume/job-application assistant, with each step (tailor, render, letter, prep, gap) earning a place. The count is only slightly inflated by tailor_resume_pdf, which is effectively a composition of three existing tools.
The lifecycle is largely covered: analyze the gap, tailor the resume, render to PDF/DOCX, write a cover letter, and prepare for interviews, with clear free-tier/no-key behavior documented. Minor gaps remain—no standalone resume parsing/validation tool and no way to store or compare multiple postings/applications—but agents can work around these by passing text or base64 files.
Writes a cover letter (or a short hiring-manager letter) from the resume for one posting. Every sentence is checked against the resume; unsupported sentences are removed and counted by reason. Returns only a letter, never other text. Review before use. Without a key: one of the conversation's 2 free AI calls (posting_text only; job_url needs a key).
Inputs: on_behalf_of (required), kind, resume, job_url, +6 more.
Likely interview questions for one job posting, each with an answer outline built ONLY from the resume: every point cites the resume line it comes from and passed the CareerGenius fact check (no invented experience; failing points are removed and listed by reason). Resume as plain text, a PDF/DOCX file (base64) or structured JSON. Counts one call and uses the AI cap. Without a key: one of the conversation's 2 free AI calls (posting_text only; job_url needs a key). Without a key the whole request is limited to 96 KB.
Inputs: on_behalf_of (required), links, resume, contact, +5 more.
Lists the keywords a job posting asks for (named tools, products, credentials, plus any you supply) and which of them the resume shows or does not show. Deterministic, no AI. Not a score and not a prediction of screening or interview outcomes. Without a key: one of the conversation's 10 free keyword/PDF calls. job_url needs a key.
Inputs: on_behalf_of (required), resume, job_url, keywords, +3 more.
The same resume layout as an editable Word (.docx) file, for a person who wants to change it before sending. Returns the file as an embedded resource (base64); nothing is stored. No AI. Counts one call of the monthly allowance. Without a key: one of the conversation's 10 free keyword/PDF calls.
Inputs: resume (required), on_behalf_of (required), template, page_size.
Lays out a structured resume as a clean, single-column, ATS-readable PDF (real selectable text, embedded fonts, no images, no tables, no clickable links), letter or A4, at most 3 pages. Returns the file as an embedded resource (base64); nothing is stored. No AI: the text is printed exactly as sent. Counts one call of the monthly allowance. Without a key: one of the conversation's 10 free keyword/PDF calls.
Inputs: resume (required), on_behalf_of (required), template, page_size.
Rewords the resume summary and job bullets for one posting. Every rewrite is checked against the resume; rewrites with unsupported claims (added tools, numbers, seniority, results or employers) are removed and listed in held_back by reason code. Every result has "outcome": which lines were reworded, kept or held back, reason codes (such as NO_CHANGES, AI_KEPT_WORDING, ALL_HELD_BACK, MISSING_KEYWORDS_NOT_ADDABLE), the posting keywords the resume does not show (rewording cannot add them), a plain explanation and next steps. Review before use. Without a key: one of the conversation's 2 free AI calls (posting_text only; job_url needs a key).
Inputs: resume (required), on_behalf_of (required), job_url, posting_text, +1 more.
One call: give a job posting (text or a public link) and the person's resume (plain text, a PDF or DOCX file in base64, or structured JSON). CareerGenius reads the posting, lists the keywords it asks for and which the resume shows, rewords the summary and bullets for this job, keeps ONLY the rewordings that pass its fact check (no added tools, numbers, titles, results or employers), and returns a clean single-column ATS-readable PDF plus a summary of what changed, what was held back and why. "outcome" explains the result in reason codes and plain words, also when nothing changed: lines reworded / kept / held back, posting keywords the resume does not show, and next steps. Counts one call and uses the AI cap. Review before sending. Without a key: one of the conversation's 2 free AI calls (posting_text only; job_url needs a key). Without a key the whole request is limited to 96 KB.
Inputs: on_behalf_of (required), links, resume, contact, +6 more.
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