CompleteMCP

A fully self-contained MCP server for tailoring resumes to job descriptions. Unlike a bare server, this bundle ships everything that drives output quality so results reproduce on any machine:

• The MCP server (scrape JD → tailor → validate → build DOCX)
• The full tailoring rules (rules.md, served by get_tailoring_rules)
• The candidate base resume (base_resume.json)
• The resume/JD skills (skills/, served by list_skills / get_skill)
• Seed memory summaries (memory/) and golden examples (examples/)

Requires Node.js 20+.

Setup

cd CompleteMCP
npm ci                 # or: npm install
cp .env.example .env    # then edit .env and add your Firecrawl API key

A Firecrawl API key (https://firecrawl.dev) is only needed for scraping JDs by URL. You can also paste JD text directly via the text parameter with no key.

Register with Cursor

Add to ~/.cursor/mcp.json (or a project-level .cursor/mcp.json):

{
  "mcpServers": {
    "resumemaker": {
      "command": "node",
      "args": ["/Users/sriman/Desktop/Code/CompleteMCP/index.js"]
    }
  }
}

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "resumemaker": {
      "command": "node",
      "args": ["/Users/sriman/Desktop/Code/CompleteMCP/index.js"]
    }
  }
}

Codex CLI

codex mcp add resumemaker -- node /Users/sriman/Desktop/Code/CompleteMCP/index.js

Tools (11)

Tool	Input	Output
get_jd	{url} | {text} | {file_path} (+ optional slug)	JD content, slug, filter result, output paths
get_base_resume	{}	Base resume + per-job bullet counts
set_base_resume	{resume_json}	Replace the base resume (new source of truth)
get_tailoring_rules	{}	The full ruleset from rules.md
list_skills	{}	Bundled skills + short descriptions
get_skill	{name}	Full SKILL.md for one skill
save_tailored_resume	{slug, resume_json}	Save JSON to data/
validate_resume	{json_path}	Identity / fabrication / hedging / bullet-count check
build_docx	{json_path}	Build the DOCX
list_jds	{}	List saved JDs
list_resumes	{}	List tailored JSONs + DOCXs

Agent workflow

get_tailoring_rules()         → read the rules
get_base_resume()             → read base + bullet counts
list_skills() / get_skill()   → load technique (esp. resume-tailor-fabricator)
get_jd({url|text|file_path})  → scrape/load + filter the JD
[tailor the resume JSON]      → the AI step
save_tailored_resume({...})   → write to data/
validate_resume({json_path})  → must PASS
build_docx({json_path})       → generate DOCX

Layout

CompleteMCP/
  index.js            MCP stdio server (11 tools)
  rules.md            Full tailoring rules (served by get_tailoring_rules)
  base_resume.json    Candidate base resume (source of truth)
  AGENTS.md           Agent startup instructions
  lib/                scraper.js, validator.js, builder.js, docx-helpers.js
  skills/             Bundled resume/JD skills (each a SKILL.md)
  memory/             Seed summaries of past tailorings
  examples/           Golden JD + tailored-JSON pairs
  jds/ data/ resumes/ Generated output (gitignored, auto-created)

All data stays local. data/, jds/, and resumes/ are gitignored so every clone starts with empty output folders.