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DanielDeshmukh

PROTEUS MCP Server

by DanielDeshmukh
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TypeScript
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What It Does

PROTEUS MCP wraps a 5-agent resume-matching pipeline as 6 discrete MCP tools. Paste a job description and resume into Claude Desktop / Claude Code / OpenCode — get a deterministic match score, gap analysis, bullet rewrites, and a tailored cover letter.

No vector DB. No black-box scoring. No hosted service. Just deterministic math over embeddings, exposed as protocol-level tools you can explain in an interview.

Why MCP?

MCP (Model Context Protocol) is the open standard for connecting AI assistants to external tools. This server proves you understand the protocol — stdio transport, JSON-RPC tool schemas, discrete tool boundaries — not just "I called an LLM API."

Related MCP server: Interview Prep MCP Agent

Tools

Tool

Input

Output

Latency

extract_jd_requirements

Raw JD text

Structured requirements (skills, seniority, keywords)

~3s

extract_resume_signals

Raw resume text

Structured candidate data (skills, experience, education)

~5s

score_match

Parsed JD + resume

Overall score + category breakdown

~2s

generate_gap_report

Parsed JD + resume

Matched / partial / missing requirements

~2s

match_resume_to_jd

Raw JD + resume text

Fast path — score + gaps

4-10s

match_resume_to_jd_full

Raw JD + resume text

Full pipeline + rewrites + cover letter

~90s

Quick Start

Prerequisites

Install

git clone https://github.com/DanielDeshmukh/proteus-mcp.git
cd proteus-mcp
npm install

Configure Environment

export NVIDIA_NIM_API_KEY=nvapi-your-key
export GROQ_API_KEY=gsk-your-key

Run

npm run dev    # development (tsx hot-reload)
npm run build  # production build
npm start      # production run

MCP Client Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "proteus": {
      "command": "node",
      "args": ["--import", "tsx", "/absolute/path/to/proteus-mcp/src/server.ts"],
      "env": {
        "NVIDIA_NIM_API_KEY": "nvapi-your-key",
        "GROQ_API_KEY": "gsk-your-key"
      }
    }
  }
}

Claude Code / OpenCode

{
  "mcpServers": {
    "proteus": {
      "command": "node",
      "args": ["--import", "tsx", "/absolute/path/to/proteus-mcp/src/server.ts"],
      "env": {
        "NVIDIA_NIM_API_KEY": "nvapi-your-key",
        "GROQ_API_KEY": "gsk-your-key"
      }
    }
  }
}

Tool Schemas

Input:

{ "jd_text": "Google — Senior Software Engineer..." }

Output:

{
  "title": "Senior Software Engineer, Cloud Platform",
  "company": "Google",
  "seniority_level": "senior",
  "hard_skills": ["Go", "Python", "Kubernetes", ...],
  "soft_skills": ["leadership", "communication", ...],
  "domain_keywords": ["distributed systems", "cloud infrastructure", ...],
  "ats_bait": ["Kubernetes", "Terraform", "gRPC", ...],
  "requirements_summary": "5+ years experience in distributed systems..."
}

Input:

{ "resume_text": "Jane Smith\njane@email.com..." }

Output:

{
  "name": "Jane Smith",
  "skills": ["Python", "Go", "Kubernetes", ...],
  "experience": [{ "role": "Senior SWE", "company": "Meta", "bullets": [...] }],
  "projects": [...],
  "education": [{ "degree": "MS CS", "institution": "Stanford" }],
  "certifications": [...]
}

Input:

{
  "jd_text": "Google — Senior Software Engineer...",
  "resume_text": "Jane Smith\njane@email.com..."
}

Output:

{
  "overall_score": 0.7966,
  "section_scores": {
    "hard_skills": 0.6571,
    "soft_skills": 1.0,
    "domain_keywords": 0.84,
    "ats_bait": 1.0
  },
  "gap_analysis": {
    "matched": 11,
    "partial": 4,
    "missing": 4,
    "total": 19,
    "gaps": [
      {
        "requirement": "Kubernetes",
        "status": "matched",
        "score": 0.95,
        "evidence": "Led migration of 200+ microservices from ECS to Kubernetes",
        "category": "hard_skill"
      }
    ]
  },
  "timings": { "parse": "4.7s", "gap_analysis": "1.9s", "aggregate": "0.0s", "total": "6.6s" }
}

Input:

{
  "jd_text": "Google — Senior Software Engineer...",
  "resume_text": "Jane Smith\njane@email.com...",
  "cover_letter_tone": "professional"
}

Output: Everything from match_resume_to_jd plus:

{
  "rewrite_suggestions": {
    "suggestions": [
      {
        "original": "Built monitoring dashboards",
        "rewrite": "Built real-time monitoring dashboards using Prometheus and Grafana, reducing mean-time-to-detection by 40%",
        "rationale": "Added specific tools from JD and quantified impact",
        "target": "Experience with observability (Prometheus, Grafana)",
        "impact": 0.85
      }
    ],
    "hidden_experience": ["Distributed tracing with OpenTelemetry"]
  },
  "cover_letter": {
    "job_title": "Senior Software Engineer",
    "full_letter": "Dear Hiring Manager,\n\nI am writing to express my interest...",
    "tone": "professional",
    "word_count": 342,
    "key_points_addressed": ["Kubernetes", "distributed systems", "observability"]
  }
}

Determinism

Component

Deterministic?

Why

aggregateScores

Yes

Pure math — weighted category scoring, no LLM

analyzeGaps (embeddings)

Yes

Cosine similarity — no temperature, no sampling

parseJd

Near-yes

Temperature pinned to 0; verified identical JSON on repeat

parseResume

Near-yes

Temperature pinned to 0; verified identical JSON on repeat

suggestRewrites

No

Temperature 0.3, creative generation

generateCoverLetter

No

Temperature 0.4, creative generation

The fast-path pipeline (match_resume_to_jd) is effectively deterministic — identical inputs produce identical scores and gap counts across repeated runs.

Scoring Formula

overall = hard_skills(50%) + domain_keywords(20%) + soft_skills(15%) + ats_bait(15%)

category_score = (matched * 1.0 + partial * 0.6) / total

Latency

Measured with real JD + resume pairs (Google Cloud SRE role vs. 7-year backend engineer):

Stage

Cold Start

Warm

Parse JD + Resume (parallel)

4.7s

2-3s

Gap Analysis

1.9s

1-2s

Aggregate (pure math)

0.0s

0.0s

Total (fast path)

6.6s

4-5s

Rewrite + Cover Letter

+20-40s

+15-30s

Total (full pipeline)

~90s

~60s

Architecture

proteus-mcp/
├── src/
│   ├── server.ts                    # MCP server entrypoint, tool registration
│   ├── test.ts                      # End-to-end integration test
│   └── tools/
│       ├── extractJdRequirements.ts # wraps parseJd()
│       ├── extractResumeSignals.ts  # wraps parseResume()
│       ├── scoreMatch.ts            # wraps analyzeGaps() + aggregateScores()
│       ├── generateGapReport.ts     # wraps analyzeGaps()
│       ├── matchResumeToJd.ts       # fast path: parse → gap → aggregate
│       └── matchResumeToJdFull.ts   # full pipeline with rewrites + cover letter
├── .github/workflows/ci.yml        # CI: build, lint, typecheck, test, security
├── models.json                      # PROTEUS model configuration
├── package.json
└── tsconfig.json

CI/CD

GitHub Actions runs on every push and PR:

Job

What it does

Build & Typecheck

tsc --noEmit + tsc across Node 18/20/22

Lint

ESLint with TypeScript rules

Test

MCP server startup verification across Node 18/20/22

Security Audit

npm audit --audit-level=high

Secret Scan

Scans source for hardcoded API keys

Privacy

  • No persistence — resume/JD text never written to disk or logs

  • No auth — local-only, single-user, no multi-tenant overhead

  • No vector DB — on-the-fly embedding comparison, not stored

  • No remote transport — stdio only, no SSE/HTTP exposure

  • Calls pipeline functions directly — bypasses Next.js API routes and database

Topics

mcp model-context-protocol resume-matching jd-analysis resume-parser career-tools nvidia-nim embeddings cosine-similarity deterministic-scoring ai-tools llm typescript claude-desktop claude-code opencode

Related Projects

  • PROTEUS — The full JD-aware resume matching pipeline with web UI, auth, and history

  • MCP SDK — Official TypeScript SDK for Model Context Protocol

License

MIT

This server cannot be installed

F
license - not found
-
quality - not tested
B
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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