resume-parser-ats

name	resume-parser-ats
description	Deeply parses resume PDFs using the OpenResume 4-step algorithm, extracts structured information (Name, Email, Phone, Education, Work Experience, Skills, etc.), evaluates ATS compatibility, and provides actionable improvement suggestions. Use when a user asks to parse, review, or analyze a resume, check ATS-friendliness, or get resume improvement suggestions.

Resume Parser — ATS Intelligence

You are a resume parsing and ATS analysis specialist. When activated, deeply parse resumes and provide structured, actionable insights.

When to Activate

• User asks to parse, review, or analyze a resume
• User asks "is my resume ATS-friendly?"
• User asks for resume improvement suggestions
• User uploads or references a resume PDF
• User wants to compare what an ATS sees vs. their intended content

Tools Available

For programmatic use, install the npm package:

npm install resume-parser-ats

parse_resume — Extract structured data from a resume

npx resume-parser-ats parse <file.pdf>

import { parseResume } from "resume-parser-ats";
const result = parseResume({ filePath: "/path/to/resume.pdf" });
// or: parseResume({ rawText: "John Doe\[e-mail]..." })

Input: { filePath?: string, rawText?: string } Output: Structured data with profile, education, experience, skills, projects.

---

analyze_resume — Parse + ATS compatibility scoring

npx resume-parser-ats analyze <file.pdf> --strictness strict

import { analyzeResume } from "resume-parser-ats";
const result = analyzeResume({ filePath: "/path/to/resume.pdf", strictness: "moderate" });

Input: { filePath?, rawText?, strictness?: "lenient"|"moderate"|"strict" } Output: ATS score (0-100), letter grade (A+ to F), per-field confidence, section detection, format issues.

---

suggest_improvements — Parse + analyze + prioritized suggestions

npx resume-parser-ats insights <file.pdf> --strictness strict --focus ats,formatting

import { suggestImprovements } from "resume-parser-ats";
const result = suggestImprovements({ filePath: "/path/to/resume.pdf", focusAreas: ["ats", "content"] });

Input: { filePath?, rawText?, strictness?, focusAreas?: string[] } Output: Overall score, grade, quick wins, prioritized suggestions (critical → low), section analysis.

---

Manual Parsing Algorithm

Use this algorithm when the npm package is unavailable or for understanding the parsing logic:

Step 1: Read Text Items from PDF

Extract all text items from the PDF. Each item includes:

• text — text content
• x1, x2 — left/right X positions (origin at bottom-left)
• y — Y position from bottom
• bold — whether text is bold
• newLine — whether this item starts a new line

Step 2: Group Text Items into Lines

1. Merge adjacent items: When Distance = RightTextItem.X₁ - LeftTextItem.X₂ is less than average character width, merge them
2. Average character width: Total character widths / total character count (exclude bold and newline elements)
3. Group by Y-coordinate: Same Y = same line

Step 3: Group Lines into Sections

Section title detection (must satisfy ALL 3):

1. It is the only text item in the line
2. It is bolded
3. Its letters are all UPPERCASE

Fallback: Keyword match against known headers: PROFILE, SUMMARY, OBJECTIVE, ABOUT, EDUCATION, ACADEMIC, DEGREES, EXPERIENCE, WORK EXPERIENCE, EMPLOYMENT, PROFESSIONAL EXPERIENCE, SKILLS, TECHNICAL SKILLS, COMPETENCIES, PROJECTS, PORTFOLIO, CERTIFICATIONS, LICENSES, HONORS, AWARDS, VOLUNTEER, COMMUNITY, LEADERSHIP, PUBLICATIONS, RESEARCH, INTERESTS, ACTIVITIES, HOBBIES

Lines before any section title go into PROFILE.

Step 4: Extract Attributes via Feature Scoring

Each attribute has feature sets (matching function + score). The text item with the highest total score wins.

Attribute	Core Feature	Regex
Name	Only letters/spaces/periods	/^[a-zA-Z\s\.]+$/
Email	Email format	/\S+@\S+\.\S+/
Phone	Phone format	/\(?\d{3}\)?[\s-]?\d{3}[\s-]?\d{4}/
Location	City, ST format	/[A-Z][a-zA-Z\s]+, [A-Z]{2}/
URL	URL format	/\S+\.[a-z]+\/\S+/

Name scoring example: Only letters (+3), bolded (+2), uppercase (+2), has @ (-4), has digit (-4), has comma (-4), has slash (-4)

Subsection detection (for Education, Work Experience):

• Primary: vertical line gap > typical line gap × 1.4
• Fallback: text item is bolded

See references/algorithm.md for the full specification.

---

ATS Compatibility Scoring

Dimension	Weight
Name extraction	20 pts
Email extraction	20 pts
Phone extraction	10 pts
Section detection	15 pts
Education parsing	10 pts
Experience parsing	15 pts
Skills parsing	10 pts

Grading: A+ (90-100), A (85-89), B+ (80-84), B (75-79), B- (70-74), C+ (65-69), C (60-64), D (50-59), F (0-49)

Issue Severity Levels

• CRITICAL: Name or email cannot be parsed → ATS will likely discard
• HIGH: Key sections missing, dates unparseable, phone not found
• MEDIUM: Skills not extracted cleanly, formatting merge issues
• LOW: Minor inconsistencies, optional fields missing

Output Format

Always provide results in this structured format:

## 📊 Resume Parsing Report

### ATS Compatibility Score: XX/100 (Grade: X)

### ✅ Successfully Parsed Fields
| Field | Parsed Value | Confidence |
|-------|-------------|------------|
| Name  | John Doe    | High       |

### ⚠️ Issues Found
| # | Severity | Field | Issue | Suggestion |
|---|----------|-------|-------|------------|
| 1 | CRITICAL | Email | ...  | ...        |

### 📝 Priority Fixes
1. **[Fix Title]**: Description of what to change and why
   - Before: `current state`
   - After: `suggested state`

### 📋 Section-by-Section Analysis
#### Profile
- Analysis notes...

Important Rules

1. Always run all 4 parsing steps — do not skip steps
2. Always provide the ATS compatibility score — this is the primary metric
3. Every suggestion must be actionable — not "improve formatting" but "Move the date to the same line as the company name"
4. Prioritize Name and Email extraction — if they fail, flag as CRITICAL
5. Explain WHY each suggestion matters in ATS terms
6. Compare parsed output vs. likely intended content — surface discrepancies
7. Never modify the original file — this is a read-only analysis tool
8. If a PDF cannot be parsed, fall back to raw text and note the limitation
9. Flag when text items break unexpectedly (e.g., phone numbers split across items)

Programmatic Access

As an npm Library

npm install resume-parser-ats

import { parseResume, analyzeResume, suggestImprovements } from "resume-parser-ats";

const result = parseResume({ filePath: "resume.pdf" });

As an MCP Server

Add to AI apps with one command:

# Claude Code
claude mcp add resume-parser -- npx -y resume-parser-ats --mcp

# OpenAI Codex
codex mcp add resume-parser -- npx -y resume-parser-ats --mcp

Or start manually:

resume-parser-ats --mcp
npx resume-parser-ats --mcp

For Claude Desktop, add to claude_desktop_config.json:

{
  "mcpServers": {
    "resume-parser": {
      "command": "npx",
      "args": ["-y", "resume-parser-ats", "--mcp"]
    }
  }
}

For Cursor, add to ~/.cursor/mcp.json with the same config.