Litjob AI job application assistant: job matches, tailored CVs and cover letters. You submit.
Scored across 13 tools
Most tools target a distinct resource and action, with the keyword pair (extract_job_keywords vs check_cv_keywords) and application pair (tailor_application vs get_application) clearly separated by their descriptions. The one soft overlap is add_job vs save_job, both of which put a job into the user's account, though the descriptions distinguish the intent (matchable-added vs saved).
Every tool follows a consistent snake_case verb_noun pattern (add_job, get_application, list_my_applications, mark_application_submitted). No camelCase or mixed-verb deviations.
13 tools is well within the sweet spot and each maps to a concrete workflow step (discover, add/save, tailor, draft answers, track, check credits). No obviously redundant tool bloats the surface.
The core job-search lifecycle is well covered: job discovery, matching, keyword analysis, tailoring, answer drafting, application tracking and credit checks. Minor gaps remain (e.g. updating the profile via get_my_profile's counterpart, or unsaving/deleting jobs), but agents can work around these without hard failures.
Adds a job the user found somewhere else (for example a pasted job ad) to their Litjob account, so it can be matched and tailored for. Only this user can see jobs they add. Free.
Inputs: title (required), company (required), description (required), location, +2 more.
Which skills and keywords from a job ad are missing from the user's CV (their latest CV in Litjob, unless cv_text is given). Free: no credits used.
Inputs: job_id, cv_text, job_text.
Drafts answers to up to 6 questions from a job's application form, using the user's real experience. Uses 1 AI credit per batch, so confirm with the user first. The user should check and edit the answers before using them.
Inputs: questions (required), application_id (required).
The skills, tools and hard requirements (experience, degree, right to work, location, languages) in a job ad, split into required and nice to have. Free.
Inputs: job_id, job_text.
The jobs that best match the user's Litjob profile right now, strongest first, with a 0–100 match score, strengths and gaps. Jobs the user already tailored an application for are left out.
Inputs: limit.
One application: the tailored CV and cover letter (Markdown), the summary, the Review score and findings, and any drafted answers.
Inputs: application_id (required).
How many AI credits the user has and whether they are on Litjob Pro. Tailoring a CV and cover letter uses 2 credits; drafting answers uses 1.
Inputs: no named parameters.
Full details of one job, including its description and how well it matches the user.
Inputs: job_id (required).
The user's Litjob career profile: headline, target roles, locations, skills and experience.
Inputs: no named parameters.
The user's applications in Litjob (tailored, in progress and submitted), newest first.
Inputs: no named parameters.
Records that the user submitted an application themselves. Only call this after the user says they submitted it: Litjob never submits applications. Free.
Inputs: application_id (required).
Saves a job to the user's saved jobs in Litjob. Free.
Inputs: job_id (required).
Starts tailoring the user's CV and writing a cover letter for one job, using only their real experience. Uses 2 AI credits, so confirm with the user first. Takes about a minute: then call get_application with the returned application_id. It does not apply for the job.
Inputs: job_id (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Auto-apply to jobs: matches your CV, tailors a fresh CV per posting, and applies for you.
Tailored, graded job applications: a CV, cover letter and form answers built per vacancy.
Find jobs, then get a tailored résumé and cover letter for each one. Tracks your applications too.
AI job matching in your assistant: ranked offers, tailored resumes, application tracking.
Add one secure layer between your agents and this server.
