# io.github.vpatser1/resume-optimizer (npm · resume-optimizer)

Resume analysis, ATS compatibility checks, bullet improvements, job-tailored optimization

- Trust score: 61/100 (medium)
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-07

## Components

- npm · `resume-optimizer`: 61/100 (this document), [markdown](https://verifymcp.io/servers/vpatser1-resume-optimizer/resume-optimizer.md), [page](https://verifymcp.io/servers/vpatser1-resume-optimizer/resume-optimizer)

## Channel facts

- Registry: `npm`
- Package: `resume-optimizer`
- Version: `1.1.4`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-07.

- **Supply Chain Security**: 87/100
  - No malware found by supply-chain analysis.
  - Known CVEs were checked across the 95 of 99 dependencies we could resolve, so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Dependency health was assessed across the 95 of 99 dependencies we could resolve, so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 19/100
  - Repository check failed: no source repository is declared.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 3 days ago).
  - Security-disclosure policy not yet verified: we couldn't inspect the source repository.
- **Schema Quality & AI Usability**: 80/100
  - AI-judged instruction clarity (excellent).
  - Tool/resource definitions use about 631 tokens (~105/item across 6 items; 6 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 0/100
  - Stability not yet verified: not enough scan history yet (needs a 30-day window).
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

**Unverified: 1 category.** A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

## Install

### Claude

```bash
claude mcp add vpatser1-resume-optimizer -- npx -y resume-optimizer
```

### Codex

```bash
codex mcp add vpatser1-resume-optimizer -- npx -y resume-optimizer
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "vpatser1-resume-optimizer": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "resume-optimizer"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add vpatser1-resume-optimizer --command npx --arg -y --arg resume-optimizer
```

### Hermes

```yaml
mcp_servers:
  vpatser1-resume-optimizer:
    command: "npx"
    args: ["-y", "resume-optimizer"]
```

### Other

```json
{
  "mcpServers": {
    "vpatser1-resume-optimizer": {
      "command": "npx",
      "args": [
        "-y",
        "resume-optimizer"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-04 (score 61)

First indexed and scored.

## MCP tools (6)

### `analyze_resume` (~106 tokens)

Perform a comprehensive resume analysis. Returns an overall score (0-100), ATS compatibility score, keyword density analysis, action verb usage, quantified achievements count, section completeness check (contact, summary, experience, education, skills), formatting/structure issues, and length assessment. Optionally score against a job description for keyword relevance.

Input parameters:

- `job_description` (string): Optional job description to score keyword relevance against
- `resume_text` (string, required): The full text content of the resume to analyze

### `optimize_for_job` (~97 tokens)

Match a resume against a specific job description. Identifies matching keywords, finds missing keywords from the job description, suggests bullet point rewrites to match job requirements, calculates a match score (0-100), and highlights transferable skills. Returns detailed gap analysis and actionable improvement suggestions.

Input parameters:

- `job_description` (string, required): The full text of the job description to optimize against
- `resume_text` (string, required): The full text content of the resume

### `generate_summary` (~108 tokens)

Generate 2-3 sentence professional summary options from resume text. Produces multiple variants -- confident, technical, and leadership-focused -- tailored to the detected career level (entry/mid/senior/executive). Optionally target a specific role for more focused output.

Input parameters:

- `resume_text` (string, required): The full text content of the resume to generate a summary from
- `target_role` (string): The target job title or role to tailor the summary toward (e.g., 'Engineering Manager')

### `improve_bullets` (~116 tokens)

Analyze resume bullet points and rewrite them for maximum impact. Checks each bullet for action verb usage, quantified results, specificity, STAR method format (Situation, Task, Action, Result), and appropriate length. Returns before/after comparisons with per-bullet scores and rewrite suggestions using stronger action verbs and quantification.

Input parameters:

- `bullets` (array, required): Array of bullet point strings to analyze and improve
- `context` (string): Optional context about the role or industry for more relevant suggestions (e.g., 'software engineering', 'marketing manager')

### `check_ats` (~91 tokens)

Deep ATS (Applicant Tracking System) compatibility check. Validates font/formatting recommendations, section header standardization, file format advice, keyword stuffing detection, and contact info parsing. Returns categorized issues (error/warning/info) with fix suggestions, detected vs missing sections, and an overall ATS-readiness rating.

Input parameters:

- `resume_text` (string, required): The full text content of the resume to check for ATS compatibility

### `analyze_keywords` (~113 tokens)

Extract and analyze keywords from resume text. Identifies hard skills, soft skills, tools/technologies, certifications, and action verbs. Assesses industry relevance and keyword density. Optionally target a specific industry (tech, finance, marketing, healthcare) for tailored analysis.

Input parameters:

- `industry` (string): Target industry for keyword relevance (e.g., 'tech', 'finance', 'marketing', 'healthcare'). Defaults to general.
- `resume_text` (string, required): The full text content of the resume to analyze for keywords

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/vpatser1-resume-optimizer/resume-optimizer#diagnostics

## Score history

- 2026-08-07: 61
- 2026-08-06: 61
- 2026-08-05: 61
- 2026-08-04: 61

## Links

- npm package: https://www.npmjs.com/package/resume-optimizer
- Socket report: https://socket.dev/npm/package/resume-optimizer
- Website: https://aivp-mcp.vercel.app/
- Changelog RSS feed: https://verifymcp.io/servers/vpatser1-resume-optimizer/resume-optimizer.xml
- Changelog JSON feed: https://verifymcp.io/servers/vpatser1-resume-optimizer/resume-optimizer.json
- HTML version of this page: https://verifymcp.io/servers/vpatser1-resume-optimizer/resume-optimizer