resume-mcp

MCP server for maintaining a professional profile database and generating tailored, ATS-optimized resumes for specific job postings.

Features

• Profile database — SQLite store for contact info, education, work experience, projects, and skills with proficiency ratings
• Job scraping — fetch and parse job postings from URLs; extract tech stack, experience level, requirements, and salary automatically
• Tailored resume generation — scores every experience and project against the job, selects the most relevant ones, and produces a clean LaTeX resume
• Relevance scoring — deterministic match score based on tech overlap, keyword coverage, and description similarity
• ATS/keyword analysis — identifies keyword gaps, naming mismatches (React vs React.js), weak action verbs, bullets lacking metrics, and unused profile items that could strengthen the resume
• No extra API keys — all analysis is pure Python (regex + keyword matching). The calling LLM (Claude, GPT, etc.) does the creative rewriting using its own subscription
• Application tracker — auto-logs every generated resume to a formatted applications.xlsx spreadsheet with color-coded scores and status tracking

How tailoring works

Each experience and project is scored 0–1 against the job posting:

Signal	Weight
Tech stack tag overlap	40%
Keyword match against requirements	35%
Description keyword match	25%

Items are ranked by score, the top 4 experiences and top 4 projects are selected, and skills matching the JD are reordered to appear first.

Review pipeline

1. full_pipeline(url) — scrapes job → generates initial resume → runs deterministic analysis in one call
2. The analysis returns: keyword gaps, ATS issues (exact phrasing mismatches), weak verb flags, bullets missing metrics, and unused profile items from the DB
3. The calling LLM acts as a hiring manager and rewrites bullets, weaves in missing keywords, reorders sections
4. save_optimized_resume(...) — saves the LLM-rewritten LaTeX back to the DB and compiles to PDF

No API keys are consumed in step 2. The LLM you're already talking to does the creative work.

Setup

cd ~/resume-mcp
pip install -r requirements.txt

# seed_profile.py is a template — open it and replace the placeholder data
# with your own info, then run it to populate the database:
python seed_profile.py

Tip: To keep your personal data out of git, copy the template to a private file:

cp seed_profile.py seed_yourname.py
echo "seed_yourname.py" >> .gitignore
python seed_yourname.py

Optional: Install pdflatex to compile resumes to PDF locally. Without it, .tex files are saved and can be compiled via Overleaf.

Claude Desktop config

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "resume-builder": {
      "command": "python3",
      "args": ["/Users/your-username/resume-mcp/server.py"]
    }
  }
}

Data storage

Path	Contents
~/.resume-mcp/profile.db	SQLite profile database
~/.resume-mcp/generated/	Generated .tex and .pdf files
~/.resume-mcp/applications.xlsx	Application tracker spreadsheet

MCP tools

Profile

Tool	Description
get_profile	Return full profile (contact, education, experience, projects, skills)
set_contact_info	Set name, email, phone, LinkedIn, GitHub, website, location
add_experience	Add a work experience entry with bullet points and tech tags
update_experience	Update fields on an existing experience by ID
delete_experience	Remove an experience by ID
add_project	Add a project with tech stack, bullets, URL, and status
update_project	Update fields on an existing project by ID
delete_project	Remove a project by ID
add_education	Add an education entry with GPA, coursework, honors
add_skill	Add or update a skill with category and proficiency (1–5)
remove_skill	Remove a skill
bulk_add_skills	Add multiple skills at once via {"Category": ["Skill1", "Skill2"]}

Job scraping

Tool	Description
scrape_job_posting	Fetch a job posting URL and extract structured data
analyze_job_from_text	Parse a job from pasted text when URL scraping fails

Resume generation

Tool	Description
generate_resume	Generate a tailored resume given job details
scrape_and_generate	One-shot: scrape URL and immediately generate resume
match_score	Check profile-to-job match without generating a resume
get_resume_latex	Retrieve full LaTeX source of a previously generated resume
list_generated_resumes	List recent resumes with job details and match scores

Review & optimization

Tool	Description
full_pipeline	Scrape → generate → analyze in one call; returns everything the LLM needs to rewrite
review_resume_against_job	Run deterministic analysis on a resume by ID or raw LaTeX
get_resume_with_context	Get resume LaTeX + job info + full profile in one call for rewriting
save_optimized_resume	Save LLM-rewritten LaTeX back to DB and compile to PDF

Application tracker

Tool	Description
log_application	Manually log an application to the spreadsheet
update_application_status	Update status (Generated → Applied → Interview → Offer/Rejected)
get_application_stats	Summary stats: total, by-status breakdown, average scores

Usage examples

These are natural language commands you'd send to Claude with the MCP server connected:

Generate a resume for this job posting: [paste URL]

Scrape this job and run the full pipeline so you can optimize my resume: [URL]

Add a new experience: I worked at Acme Corp as a backend engineer from Jan 2024 to present.
Bullets: [...]

What's my match score for a job that requires React, TypeScript, and AWS experience?

Update my application status for row 3 to "Interview"

Show me my application stats